Encyclopedia
Text Classification Algorithms: A Survey
Referenced paper:
Please note this is a comparison between Version 3 by Kamran Kowsari and Version 7 by Kamran Kowsari.
In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluation methods. Finally, the limitations of each technique and its application in real-world problems are discussed.
- text classification
- text mining
- text representation
- text categorization
- text analysis
- document classification
Introduction
Text and Document Feature Extraction
Text feature extraction and pre-processing for classification algorithms are very significant. In this section, we start to talk about text cleaning since most of the documents contain a lot of noise. In this part, we discuss two primary methods of text feature extractions- word embedding and weighted word.
Text Cleaning and Pre-processing
In Natural Language Processing (NLP), most of the text and documents contain many words that are redundant for text classification, such as stopwords, miss-spellings, slangs, and etc. In this section, we briefly explain some techniques and methods for text cleaning and pre-processing text documents. In many algorithms like statistical and probabilistic learning methods, noise and unnecessary features can negatively affect the overall performance. So, tee limination of these features is extremely important.
Tokenization
Tokenization is the process of breaking down a stream of text into words, phrases, symbols, or any other meaningful elements called tokens. The main goal of this step is to extract individual words in a sentence. Along with text classification, in text mining, it is necessary to incorporate a parser in the pipeline which performs the tokenization of the documents; for example:
:sentence:
After sleeping for four hours, he decided to sleep for another four
:
After sleeping for four hours, he decided to sleep for another four
In this case, the tokens are as follows:
{'After', 'sleeping', 'for', 'four', 'hours', 'he', 'decided', 'to', 'sleep', 'for', 'another', 'four'}
:
{'After', 'sleeping', 'for', 'four', 'hours', 'he', 'decided', 'to', 'sleep', 'for', 'another', 'four'}
Here is python code for Tokenization:
:
from mnltk.ktokenize eimport tword_tokenize
etext t= ="After sleeping for four hours, he decided to sleep for another four"
"tokens s= =word_tokenize(etext)
t)print(ttokens)sStop words
Text and document classification over social media, such as Twitter, Facebook, and so on is usually affected by the noisy nature (abbreviations, irregular forms) of the text corpora.
.Here is an example from geeksforgeeks
from mnltk.kcorpus simport tstopwords
sfrom mnltk.ktokenize eimport tword_tokenize
e
example_sent t= ="This is a sample sentence, showing off the stop words filtration."
"
stop_words s= =set(tstopwords.swords(s'english'))
'))
word_tokens s= =word_tokenize(eexample_sent)
t)
filtered_sentence e= [= w wfor rw win nword_tokens sif fnot tw win nstop_words]
s]
filtered_sentence e= []
= []
for rw win nword_tokens:
s:
if fw wnot tin nstop_words:
s:
filtered_sentence.eappend(dw)
w)
print(tword_tokens)
s)print(tfiltered_sentence)eOutput:
:
['This', ','is', ','a', ','sample', ','sentence', ',',', ','showing',
','off', ','the', ','stop', ','words', ','filtration', ','.']
[']
'This', ','sample', ','sentence', ',',', ','showing', ','stop',
','words', ','filtration', ','.']'Capitalization
Sentences can contain a mixture of uppercase and lower case letters. Multiple sentences make up a text document. To reduce the problem space, the most common approach is to reduce everything to lower case. This brings all words in a document in the same space, but it often changes the meaning of some words, such as "US" to "us" where the first one represents the United States of America and the second one is a pronoun. To solve this, slang and abbreviation converters can be applied.
.
text t= ="The United States of America (USA) or America, is a federal republic composed of 50 states"
"print(ttext)
t)print(ttext.tlower())r()Output:
:
"The United States of America (USA) or America, is a federal republic composed of 50 states"
""the united states of america (usa) or america, is a federal republic composed of 50 states"Slangs and Abbreviations
Slangs and abbreviations can cause problems while executing thepre-processing steps. An abbreviation is a shortened form of a word, such as SVM stand for Support Vector Machine. Slang is a version of the language that depicts informal conversation or text that has a different meaning, such as "lost the plot", it essentially means that 'they've gone mad'. The c ommon method to deal with these words is converting them to formal language.
Noise Removal
Another issue of text cleaning as a pre-processing step is noise removal. Text documents generally containn characters like punctuations or special characters and they are not necessary for text mining or classification purposes. Although punctuation is critical to understand the meaning of the sentence, butit can affect the classification algorithms negatively.
.Here is simple code to remove standard noise from the text:
:
def ftext_cleaner(rtext):
t):
rules s= [
{= [
r'>\s+': ':u'>'}, '}, # remove spaces after a tag opens or closes
{s
r'\s+': ':u' '}, '}, # replace consecutive spaces
{s
r'\s*<br\s*/?>\s*': ':u'\n'}, '}, # newline after a <br>
{>
r'</(div)\s*>\s*': ':u'\n'}, '}, # newline after </p> and </div> and <h1/>.
{.
r'</(p|h\d)\s*>\s*': ':u'\n\n'}, '}, # newline after </p> and </div> and <h1/>.
{.
r'<head>.*<\s*(/head|body)[^>]*>': ':u''}, '}, # remove <head> to </head>
{>
r'<a\s+href="([^"]+)"[^>]*>.*</a>': ':r'\1'}, '}, # show links instead of texts
{s
r'[ \t]*<[^<]*?/?>': ':u''}, '}, # remove remaining tags
{s
r'^\s+': ':u''} '} # remove spaces at the beginning
]
g
]
for rrule ein nrules:
s:
for (r k, k,v) v)in nrule.eitems():
s():
regex x= =re.ecompile(ek)
k)
text t= =regex.xsub(bv, v,text)
t)
text t= =text.trstrip()
p()
return ntext.tlower()r(Spelling Correction
An optional part of the pre-processing step is correcting the misspelled words. Different techniques, such as hashing-based and context-sensitive spelling correction techniques, or spelling correction using trie and damerau-levenshtein distance bigram have been introduced to tackle this issue.
.
from autocorrect import spell
print tspell(l'caaaar')
')print tspell(lu'mussage')
')print tspell(lu'survice')
')print tspell(lu'hte')'
Result:
caesar
message
service
the
Stemming
Text Stemming is modifying a word to obtain its variants using different linguistic processesse like affixation (addition of affixes). For example, the stem of the word "studying" is "study", to which -ing.
.Here is an example of Stemming from NLTK
from mnltk.kstem mimport tPorterStemmer
rfrom mnltk.ktokenize eimport tsent_tokenize, e,word_tokenize
e
ps s= =PorterStemmer()
r()
example_words s= [= "python",""pythoner",""pythoning",""pythoned",""pythonly"]
"]
for rw win nexample_words:
s:print(tps.sstem(mw))w)Result:
python
python
python
python
pythonli
Lemmatization
Text lemmatization is the process of eliminating redundant prefix or suffix of a word and extract the base word (lemma).
.
from mnltk.kstem mimport tWordNetLemmatizer
r
lemmatizer r= =WordNetLemmatizer()
r()
print(tlemmatizer.rlemmatize(e"cats"))")Word Embedding
Different word embedding procedures have been proposed to translate these unigrams into consummable input for machine learning algorithms. A very simple way to perform such embedding is term-frequency y(TF) where each word will be mapped to a number corresponding to the number of occurrence of that word in the whole corpora. The other term frequency functions have been also used that represent word-frequency as a Boolean or logarithmically scaled number. Here, each document will be converted to a vector of the same length containing the frequency of the words in that document. Although such an approach may seem very intuitive. Ie but t suffers from the fact that particular words that are used very commonly in language literature might dominate this sort of word representations.
Word2Vec
Original from https://code.google.com/p/word2vec/
/I’ve copied it to a github project so that I can apply and track community patches (starting with capability for Mac OS X compilation).
- makefile and some source has been modified for Mac OS X compilation See https://code.google.com/p/word2vec/issues/detail?id=1#c5
- memory patch for word2vec has been applied See https://code.google.com/p/word2vec/issues/detail?id=2
- Project file layout altered
.
- makefile and some source has been modified for Mac OS X compilation
- See
- memory patch for word2vec has been applied
- See
- Project file layout altered
There seems to be a segfault in the compute-accuracy utility.
.To get started:
:
cd scripts && ./demo-word.sh
Original README text follows:
:This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research purposes.
.this code provides an implementation of the Continuous Bag-of-Words (CBOW) and the Skip-gram model (SG), as well as several demo scripts.
.Given a text corpus, the word2vec tool learns a vector for every word in the vocabulary using the Continuous Bag-of-Words or the Skip-Gram neural network architectures. The user should specify the following: - desired vector dimensionality (size of the context window for either the Skip-Gram or the Continuous Bag-of-Words model), training algorithm (hierarchical softmax andd//or negative sampling), the threshold for downsampling the frequent words, number of threads to use, the format of the output word vector file (text or binary).
.Usually, other hyper-parameters, such as the learning rate do not need to be tuned for different training sets.
.The script demo-word.sh downloads a small (100MB) text corpus from the webb and trains a small word vector model. After the training is finished, users can interactively explore the similarity of the words.
.More information about the scripts is provided at https://code.google.com/p/word2vec/
Global Vectors for Word Representation (GloVe)
)
An implementation of the GloVe model for learning word representations is provided, and describe how to download web-dataset vectors or train your own. See the project page or the paper for more information on glove vectors.
Contextualized Word Representations
ELMo is a deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). These word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. They can be easily added to existing models and significantly improve the state of the art across a broad range of challenging NLP problems, including question answering, textual entailment, and sentiment analysis.
.ELMo representations are:
:
- Contextual:
- The representation for each word depends on the entire context in which it is used.
- Deep:
- The word representations combine all layers of a deep pre-trained neural network.
- Character-based:
- ELMo representations are purely character-based, allowing the network to use morphological clues to form robust representations for out-of-vocabulary tokens unseen in training.
- Contextual: The representation for each word depends on the entire context in which it is used.
- Deep: The word representations combine all layers of a deep pre-trained neural network.
- Character based: ELMo representations are purely character based, allowing the network to use morphological clues to form robust representations for out-of-vocabulary tokens unseen in training.
Tensorflow implementation
nTensorflow implementation of the pre-trained biLM used to compute ELMo representations from "Deep contextualized word representations".
.This repository supports both training biLMs and using pre-trained models for prediction.
.We also have a p ytyorch implementation available in AllenNLP.
.You may also find it easier to use the version provided in Tensorflow Hub if you just like to make predictions.
.pre-trained models:
:We have got several pre-trained English language biLMs available for use. Each model is specified with two separate files, a JSON formatted "options" file with hyperparameters and aa hdf5 formatted file with the model weights. Links to the pre-trained models are available here.
.There are three ways to integrate ELMo representations into a downstream task, depending on your use case.
.
- Compute representations on the fly from raw text using character input. This is the most general method and will handle any input text. It is also the most computationally expensive.
- Precompute and cache the context-independent token representations, then compute context-dependent representations using the biLSTMs for input data. This method is less computationally expensive than #1 but is only applicable with a fixed, prescribed vocabulary.
- Precompute the representations for your entire dataset and save to a file.
- Compute representations on the fly from raw text using character input. This is the most general method and will handle any input text. It is also the most computationally expensive.
- Precompute and cache the context-independent token representations, then compute context dependent representations using the biLSTMs for input data. This method is less computationally expensive then #1, but is only applicable with a fixed, prescribed vocabulary.
- Precompute the representations for your entire dataset and save to a file.
We have used all of these methods in the past for various use cases. #1 is necessary for evaluating at test time on unseen data (e.g. public SQuAD leaderboard). #2 is a good compromise for large datasets where the size of the file in is unfeasible (SNLI, SQuAD). #3 is a good choice for smaller datasets or in cases where you'd like to use ELMo in other frameworks.
.In all cases, the process roughly follows the same steps. First, create a Batcher (or TokenBatcher for #2) to translate tokenized strings to NumPy arrays of character (or token) ids. Then, load the pre-trained ELMo model (class BidirectionalLanguageModel). Finally, for steps #1 and #2 use weight_layers to compute the final ELMo representations. For #3, use BidirectionalLanguageModel eto write all the intermediate layers to a file.
.
Architecture of the language model applied to an example sentence [Reference: arXiv paper].
FastText
fastText is a library for efficient learning of word representations and sentence classification.
.Github: facebookresearch/fastText
tModels
s
- Recent state-of-the-art
- .
- Word vectors for
- .
- Models for
- and
- .
- Recent state-of-the-art English word vectors.
- Word vectors for 157 languages trained on Wikipedia and Crawl.
- Models for language identification and
Supplementary data :
- The preprocessed YFCC100M data .
:
- The preprocessed
- .
FAQ
QYou can find answers to frequently asked questions on Their project website.
.Cheatsheet
tAlsoo a cheatsheet is provided full of useful one-liners.
Weighted Words
Term frequency
Term frequency is Bag of words that is one of the simplest techniques of text feature extraction. This method is based on counting the number of the words in each document and assigns it to feature space.
Term Frequency-Inverse Document Frequency
The mathematical representation of the weight of a term in a document by Tf-idf is given:
:
Where N is anumber of documents and df(t) is the number of documents containing the term t in the corpus. The first part would improve recall and the later would improve the precision of the word embedding. Although tf-idf tries to overcome the problem of common terms in thedocument, it still suffers from some other descriptive limitations. Namely, tf-idf cannot account for the similarity between words in the document since each word is presented as an index. In the recent years, with thedevelopment of more complex models, such as neural nets, new methods hasav been presented that can incorporate concepts, such as similarity of words and part of speech tagging. This work uses, word2vec and Glove, two of the most common methods that have been successfully used for deep learning techniques.
.
from msklearn.nfeature_extraction.ntext timport tTfidfVectorizer
rdef floadData(aX_train, n,X_test,tMAX_NB_WORDS=75000):
0):
vectorizer_x x= =TfidfVectorizer(rmax_features=MAX_NB_WORDS)
S)
X_train n= =vectorizer_x.xfit_transform(mX_train).n)toarray()
y()
X_test t= =vectorizer_x.xtransform(mX_test).t)toarray()
y()
print(t"tf-idf with","str(rnp.parray(yX_train).n)shape[e1]),1])"features")
")
return (n X_train,nX_test)tComparison of Feature Extraction Techniques
|
s
| Model |
| Advantages |
| Limitation |
| Weighted Words |
|
|
|
|
|
|
|
| TF-IDF |
|
|
|
|
|
|
| Word2Vec |
|
|
|
|
| GloVe (Pre-Trained) |
|
|
|
|
|
|
| GloVe (Trained) |
|
|
|
|
|
|
| FastText | ||
|
|
|
| Contextualized Word Representations |
|
|
| Model | Advantages | Limitation |
| Weighted Words |
|
|
| TF-IDF |
|
|
| Word2Vec |
|
|
| GloVe (Pre-Trained) |
|
|
| GloVe (Trained) |
|
|
| FastText |
|
|
| Contextualized Word Representations |
|
|
Dimensionality Reduction
Principal Component Analysis (PCA)
Principle component analysis~(PCA) is the most popular technique in multivariate analysis and dimensionality reduction. PCA is a method to identify a subspace in which the data approximately lies. This means finding new variables that are uncorrelated and maximizing the variance to preserve as much variability as possible.
.Example of PCA on text dataset (20newsgroups) from tf-idf with 75000 features to 2000 components:
:
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
def fTFIDF(FX_train, n,X_test, t,MAX_NB_WORDS=75000):
0):
vectorizer_x x= =TfidfVectorizer(rmax_features=MAX_NB_WORDS)
S)
X_train n= =vectorizer_x.xfit_transform(mX_train).n)toarray()
y()
X_test t= =vectorizer_x.xtransform(mX_test).t)toarray()
y()
print(t"tf-idf with", ",str(rnp.parray(yX_train).n)shape[e1]), 1]),"features")
")
return (n X_train, n,X_test)
t)
from msklearn.ndatasets simport tfetch_20newsgroups
s
newsgroups_train n= =fetch_20newsgroups(ssubset='train')
')newsgroups_test t= =fetch_20newsgroups(ssubset='test')
')X_train n= =newsgroups_train.ndata
aX_test t= =newsgroups_test.tdata
ay_train n= =newsgroups_train.ntarget
ty_test t= =newsgroups_test.ttarget
t
X_train,nX_test t= =TFIDF(FX_train,nX_test)
t)
from msklearn.ndecomposition nimport tPCA
Apca a= =PCA(An_components=2000)
0)X_train_new w= =pca.afit_transform(mX_train)
n)X_test_new w= =pca.atransform(mX_test)
t)
print(t"train with old features: ","np.parray(yX_train).n)shape)
e)print(t"train with new features:" ," np.parray(yX_train_new).w)shape)
e)
print(t"test with old features: ","np.parray(yX_test).t)shape)
e)print(t"test with new features:" ," np.parray(yX_test_new).w)shape)e
output:
:
tf-idf fwith h75000 0features
strain nwith hold dfeatures: (s: 11314, 4,75000)
0)train nwith hnew wfeatures: (s: 11314, 4,2000)
0)test twith hold dfeatures: (s: 7532, 2,75000)
0)test twith hnew wfeatures: (s: 7532, 2,2000)0Linear Discriminant Analysis (LDA)
Linear Discriminant Analysis (LDA) is another commonly used technique for data classification and dimensionality reduction. LDA is particularly helpful where the within-class frequencies are unequal and their performances have been evaluated on randomly generated test data. Class-dependent and class-independent transformation are two approaches in LDA where the ratio of between-class-variance to within-class-variance and the ratio of the overall-variance to within-class-variance are used respectively.
.
from msklearn.nfeature_extraction.ntext timport tTfidfVectorizer
rimport tnumpy yas snp
pfrom msklearn.ndiscriminant_analysis simport tLinearDiscriminantAnalysis
s
def fTFIDF(FX_train, n,X_test, t,MAX_NB_WORDS=75000):
0):
vectorizer_x x= =TfidfVectorizer(rmax_features=MAX_NB_WORDS)
S)
X_train n= =vectorizer_x.xfit_transform(mX_train).n)toarray()
y()
X_test t= =vectorizer_x.xtransform(mX_test).t)toarray()
y()
print(t"tf-idf with", ",str(rnp.parray(yX_train).n)shape[e1]), 1]),"features")
")
return (n X_train, n,X_test)
t)
from msklearn.ndatasets simport tfetch_20newsgroups
s
newsgroups_train n= =fetch_20newsgroups(ssubset='train')
')newsgroups_test t= =fetch_20newsgroups(ssubset='test')
')X_train n= =newsgroups_train.ndata
aX_test t= =newsgroups_test.tdata
ay_train n= =newsgroups_train.ntarget
ty_test t= =newsgroups_test.ttarget
t
X_train,nX_test t= =TFIDF(FX_train,nX_test)
t)
LDA A= =LinearDiscriminantAnalysis(sn_components=15)
5)X_train_new w= =LDA.Afit(tX_train,ny_train)
n)X_train_new w= = LDA.Atransform(mX_train)
n)X_test_new w= =LDA.Atransform(mX_test)
t)
print(t"train with old features: ","np.parray(yX_train).n)shape)
e)print(t"train with new features:" ," np.parray(yX_train_new).w)shape)
e)
print(t"test with old features: ","np.parray(yX_test).t)shape)
e)print(t"test with new features:" ," np.parray(yX_test_new).w)shape)eoutput:
tf-idf with 75000 features
train with old features: (11314, 75000)
train with new features: (11314, 15)
test with old features: (7532, 75000)
test with new features: (7532, 15)
Non-negative Matrix Factorization (NMF)
from msklearn.nfeature_extraction.ntext timport tTfidfVectorizer
rimport tnumpy yas snp
pfrom msklearn.ndecomposition nimport tNMF
F
def fTFIDF(FX_train, n,X_test, t,MAX_NB_WORDS=75000):
0):
vectorizer_x x= =TfidfVectorizer(rmax_features=MAX_NB_WORDS)
S)
X_train n= =vectorizer_x.xfit_transform(mX_train).n)toarray()
y()
X_test t= =vectorizer_x.xtransform(mX_test).t)toarray()
y()
print(t"tf-idf with", ",str(rnp.parray(yX_train).n)shape[e1]), 1]),"features")
")
return (n X_train, n,X_test)
t)
from msklearn.ndatasets simport tfetch_20newsgroups
s
newsgroups_train n= =fetch_20newsgroups(ssubset='train')
')newsgroups_test t= =fetch_20newsgroups(ssubset='test')
')X_train n= =newsgroups_train.ndata
aX_test t= =newsgroups_test.tdata
ay_train n= =newsgroups_train.ntarget
ty_test t= =newsgroups_test.ttarget
t
X_train,nX_test t= =TFIDF(FX_train,nX_test)
t)
NMF_ _= =NMF(Fn_components=2000)
0)X_train_new w= =NMF_._fit(tX_train)
n)X_train_new w= = NMF_._transform(mX_train)
n)X_test_new w= =NMF_._transform(mX_test)
t)
print(t"train with old features: ","np.parray(yX_train).n)shape)
e)print(t"train with new features:" ," np.parray(yX_train_new).w)shape)
e)
print(t"test with old features: ","np.parray(yX_test).t)shape)
e)print(t"test with new features:" ," np.parray(yX_test_new))w)output:
tf-idf with 75000 features
train with old features: (11314, 75000)
train with new features: (11314, 2000)
test with old features: (7532, 75000)
test with new features: (7532, 2000)
Random Projection
Random projection or random feature is a dimensionality reduction technique mostly used for very large volume dataset or very high dimensional feature space. Text and document, especially with weighted feature extraction, can contain a huge number of underlying features. Many researchers addressed Random Projection for text data for text mining, text classification and/or dimensionality reduction. We start to review some random projection techniques.
.
from msklearn.nfeature_extraction.ntext timport tTfidfVectorizer
rimport tnumpy yas snp
p
def fTFIDF(FX_train, n,X_test, t,MAX_NB_WORDS=75000):
0):
vectorizer_x x= =TfidfVectorizer(rmax_features=MAX_NB_WORDS)
S)
X_train n= =vectorizer_x.xfit_transform(mX_train).n)toarray()
y()
X_test t= =vectorizer_x.xtransform(mX_test).t)toarray()
y()
print(t"tf-idf with", ",str(rnp.parray(yX_train).n)shape[e1]), 1]),"features")
")
return (n X_train, n,X_test)
t)
from msklearn.ndatasets simport tfetch_20newsgroups
s
newsgroups_train n= =fetch_20newsgroups(ssubset='train')
')newsgroups_test t= =fetch_20newsgroups(ssubset='test')
')X_train n= =newsgroups_train.ndata
aX_test t= =newsgroups_test.tdata
ay_train n= =newsgroups_train.ntarget
ty_test t= =newsgroups_test.ttarget
t
X_train,nX_test t= =TFIDF(FX_train,nX_test)
t)
from msklearn nimport trandom_projection
n
RandomProjection n= =random_projection.nGaussianRandomProjection(nn_components=2000)
0)X_train_new w= =RandomProjection.nfit_transform(mX_train)
n)X_test_new w= =RandomProjection.ntransform(mX_test)
t)
print(t"train with old features: ","np.parray(yX_train).n)shape)
e)print(t"train with new features:" ," np.parray(yX_train_new).w)shape)
e)
print(t"test with old features: ","np.parray(yX_test).t)shape)
e)print(t"test with new features:" ," np.parray(yX_test_new).w)shape)eoutput:
:
tf-idf fwith h75000 0features
strain nwith hold dfeatures: (s: 11314, 4,75000)
0)train nwith hnew wfeatures: (s: 11314, 4,2000)
0)test twith hold dfeatures: (s: 7532, 2,75000)
0)test twith hnew wfeatures: (s: 7532, 2,2000)0Autoencoder
Autoencoder is a neural network technique that is trained to attempt to map its input to its output. The autoencoder as dimensional reduction methods have achieved great success via the powerful reprehensibility of neural networks. The main idea is, one hidden layer between the input and output layers with fewer neurons can be used to reduce the dimension of feature space. Specially for texts, documents, and sequences that contains many features, autoencoder could help to process data faster and more efficiently.
.
from mkeras.slayers simport tInput, t,Dense
efrom mkeras.smodels simport tModel
l
# this is the size of our encoded representations
sencoding_dim m= =1500
0
# this is our input placeholder
rinput t= =Input(tshape=(=n,))
n,))# "encoded" is the encoded representation of the input
tencoded d= =Dense(eencoding_dim, m,activation='relu')(')input)
t)# "decoded" is the lossy reconstruction of the input
tdecoded d= =Dense(en, n,activation='sigmoid')(')encoded)
d)
# this model maps an input to its reconstruction
nautoencoder r= =Model(linput, t,decoded)
d)
# this model maps an input to its encoded representation
nencoder r= =Model(linput, t,encoded)
d)
encoded_input t= =Input(tshape=(=encoding_dim,))
m,))# retrieve the last layer of the autoencoder model
ldecoder_layer r= =autoencoder.rlayers[s-1]
1]# create the decoder model
ldecoder r= =Model(lencoded_input, t,decoder_layer(rencoded_input))
t))
autoencoder.rcompile(eoptimizer='adadelta', ',loss='binary_crossentropy')'Load data:
:
autoencoder.rfit(tx_train, n,x_train,
n,
epochs=50,
0,
batch_size=256,
6,
shuffle=True,
e,
validation_data=(=x_test, t,x_test))t)T-distributed Stochastic Neighbor Embedding (T-SNE)
T-distributed Stochastic Neighbor Embedding (T-SNE) is a nonlinear dimensionality reduction technique for embedding high-dimensional data which is mostly used for visualization in a low-dimensional space. This approach is based on G. Hinton and ST. Roweis . SNE works by converting the high dimensional Euclidean distances into conditional probabilities which represent similarities.
.
Example:
:
import numpy as np
from sklearn.manifold import TSNE
X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
X_embedded = TSNE(n_components=2).fit_transform(X)
X_embedded.shape
Example of Glove and T-SNE for text:
:
Text Classification Techniques
Rocchio classification
The first version of Rocchio algorithm is introduced by rocchio in 1971 to use relevance feedback in querying full-text databases. Since then many researchers have addressed and developed this technique for text and document classification. This method uses TF-IDF weights for each informative word instead of a set of Boolean features. Using a training set of documents, Rocchio's algorithm builds a prototype vector for each class which is an average vector over all training document vectors that belongs to a certain class. Then, it will assign each test document to a class with maximum similarity that between test document and each of the prototype vectors.
.When in nearest centroid classifier, we used for text as input data for classification with tf-idf vectors, this classifier is known as the Rocchio classifier.
.
from msklearn.nneighbors.snearest_centroid dimport tNearestCentroid
dfrom msklearn.npipeline eimport tPipeline
efrom msklearn nimport tmetrics
sfrom msklearn.nfeature_extraction.ntext timport tCountVectorizer
rfrom msklearn.nfeature_extraction.ntext timport tTfidfTransformer
rfrom msklearn.ndatasets simport tfetch_20newsgroups
s
newsgroups_train n= =fetch_20newsgroups(ssubset='train')
')newsgroups_test t= =fetch_20newsgroups(ssubset='test')
')X_train n= =newsgroups_train.ndata
aX_test t= =newsgroups_test.tdata
ay_train n= =newsgroups_train.ntarget
ty_test t= =newsgroups_test.ttarget
t
text_clf f= =Pipeline([(e(['vect', ',CountVectorizer()),
(r()),
'tfidf', ',TfidfTransformer()),
(r()),
'clf', ',NearestCentroid()),
])
d()),
])
text_clf.ffit(tX_train, n,y_train)
n)
predicted d= =text_clf.fpredict(tX_test)
t)
print(tmetrics.sclassification_report(ty_test, t,predicted))d)Output:
:
precision n recall l f1-score e support
t
0 0 0.75 5 0.49 9 0.60 0 319
9
1 1 0.44 4 0.76 6 0.56 6 389
9
2 2 0.75 5 0.68 8 0.71 1 394
4
3 3 0.71 1 0.59 9 0.65 5 392
2
4 4 0.81 1 0.71 1 0.76 6 385
5
5 5 0.83 3 0.66 6 0.74 4 395
5
6 6 0.49 9 0.88 8 0.63 3 390
0
7 7 0.86 6 0.76 6 0.80 0 396
6
8 8 0.91 1 0.86 6 0.89 9 398
8
9 9 0.85 5 0.79 9 0.82 2 397
7
10 0 0.95 5 0.80 0 0.87 7 399
9
11 1 0.94 4 0.66 6 0.78 8 396
6
12 2 0.40 0 0.70 0 0.51 1 393
3
13 3 0.84 4 0.49 9 0.62 2 396
6
14 4 0.89 9 0.72 2 0.80 0 394
4
15 5 0.55 5 0.73 3 0.63 3 398
8
16 6 0.68 8 0.76 6 0.71 1 364
4
17 7 0.97 7 0.70 0 0.81 1 376
6
18 8 0.54 4 0.53 3 0.53 3 310
0
19 9 0.58 8 0.39 9 0.47 7 251
1
avg g/ /total l 0.74 4 0.69 9 0.70 0 7532
Boosting and Bagging
Boosting
g
Boosting is a Ensemble learning meta-algorithm for primarily reducing variance in supervised learning. It is basically a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Michael Kearns and Leslie Valiant (1988, 1989) Can a set of weak learners create a single strong learner? A weak learner is defined to be a Classification that is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.
.
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.pipeline import Pipeline
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', GradientBoostingClassifier(n_estimators=100)),
])
text_clf.fit(X_train, y_train)
predicted = text_clf.predict(X_test)
print(metrics.classification_report(y_test, predicted))
Output:
:
precision recall f1-score support
0 0.81 0.66 0.73 319
1 0.69 0.70 0.69 389
2 0.70 0.68 0.69 394
3 0.64 0.72 0.68 392
4 0.79 0.79 0.79 385
5 0.83 0.64 0.72 395
6 0.81 0.84 0.82 390
7 0.84 0.75 0.79 396
8 0.90 0.86 0.88 398
9 0.90 0.85 0.88 397
10 0.93 0.86 0.90 399
11 0.90 0.81 0.85 396
12 0.33 0.69 0.45 393
13 0.87 0.72 0.79 396
14 0.87 0.84 0.85 394
15 0.85 0.87 0.86 398
16 0.65 0.78 0.71 364
17 0.96 0.74 0.84 376
18 0.70 0.55 0.62 310
19 0.62 0.56 0.59 251
avg / total 0.78 0.75 0.76 7532
Bagging
from sklearn.ensemble import BaggingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', BaggingClassifier(KNeighborsClassifier())),
])
text_clf.fit(X_train, y_train)
predicted = text_clf.predict(X_test)
print(metrics.classification_report(y_test, predicted))
Output:
:
precision recall f1-score support
0 0.57 0.74 0.65 319
1 0.60 0.56 0.58 389
2 0.62 0.54 0.58 394
3 0.54 0.57 0.55 392
4 0.63 0.54 0.58 385
5 0.68 0.62 0.65 395
6 0.55 0.46 0.50 390
7 0.77 0.67 0.72 396
8 0.79 0.82 0.80 398
9 0.74 0.77 0.76 397
10 0.81 0.86 0.83 399
11 0.74 0.85 0.79 396
12 0.67 0.49 0.57 393
13 0.78 0.51 0.62 396
14 0.76 0.78 0.77 394
15 0.71 0.81 0.76 398
16 0.73 0.73 0.73 364
17 0.64 0.79 0.71 376
18 0.45 0.69 0.54 310
19 0.61 0.54 0.57 251
avg / total 0.67 0.67 0.67 7532
Naive Bayes Classifier
Naïve Bayes text classification has been used in industry and academia for a long time (introduced by Thomas Bayes between 1701-1761). However, this technique is being studied since the 1950s for text and document categorization. Naive Bayes Classifier (NBC) is generative model which is widely used in Information Retrieval. Many researchers addressed and developed this technique for their applications. We start with the most basic version of NBC which developed by using term-frequency (Bag of Word) fetaure extraction technique by counting number of words in documents
s
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', MultinomialNB()),
])
text_clf.fit(X_train, y_train)
predicted = text_clf.predict(X_test)
print(metrics.classification_report(y_test, predicted))
Output:
:
precision recall f1-score support
0 0.80 0.52 0.63 319
1 0.81 0.65 0.72 389
2 0.82 0.65 0.73 394
3 0.67 0.78 0.72 392
4 0.86 0.77 0.81 385
5 0.89 0.75 0.82 395
6 0.93 0.69 0.80 390
7 0.85 0.92 0.88 396
8 0.94 0.93 0.93 398
9 0.92 0.90 0.91 397
10 0.89 0.97 0.93 399
11 0.59 0.97 0.74 396
12 0.84 0.60 0.70 393
13 0.92 0.74 0.82 396
14 0.84 0.89 0.87 394
15 0.44 0.98 0.61 398
16 0.64 0.94 0.76 364
17 0.93 0.91 0.92 376
18 0.96 0.42 0.58 310
19 0.97 0.14 0.24 251
avg / total 0.82 0.77 0.77 7532
K-nearest Neighbor
R In machine learning, the k-nearest neighbors algorithm (kNN) is a non-parametric technique used for classification. This method is used in Natural-language processing (NLP) as a text classification technique in many researches in the past decades.
.
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', KNeighborsClassifier()),
])
text_clf.fit(X_train, y_train)
predicted = text_clf.predict(X_test)
print(metrics.classification_report(y_test, predicted))
Output:
:
precision recall f1-score support
0 0.43 0.76 0.55 319
1 0.50 0.61 0.55 389
2 0.56 0.57 0.57 394
3 0.53 0.58 0.56 392
4 0.59 0.56 0.57 385
5 0.69 0.60 0.64 395
6 0.58 0.45 0.51 390
7 0.75 0.69 0.72 396
8 0.84 0.81 0.82 398
9 0.77 0.72 0.74 397
10 0.85 0.84 0.84 399
11 0.76 0.84 0.80 396
12 0.70 0.50 0.58 393
13 0.82 0.49 0.62 396
14 0.79 0.76 0.78 394
15 0.75 0.76 0.76 398
16 0.70 0.73 0.72 364
17 0.62 0.76 0.69 376
18 0.55 0.61 0.58 310
19 0.56 0.49 0.52 251
avg / total 0.67 0.66 0.66 7532
Support Vector Machine (SVM)
The original version of SVM was introduced by Vapnik and Chervonenkis in 1963. The early 1990s, nonlinear version was addressed by BE. Boser et al. Original version of SVM was designed for binary classification problem, but Many researchers have worked on multi-class problem using this authoritative technique.
.The advantages of support vector machines are based on scikit-learn page:
:
- Effective in high dimensional spaces.
- Still effective in cases where number of dimensions is greater than the number of samples.
- Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
- Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
The disadvantages of support vector machines include:
- E
- If the number ffective in high dimensional spacesf features is much greater than the number of samples, avoiding over-fitting via choosing kernel functions and regularization term is crucia.
- SSVMs do ntill effective in cases where number of dimensions is greater than the number of samplest directly provide probability estimates, these are calculated using an expensive five-fold cross-validation (see Scores and probabilities, below.
- Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
- Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
The disadvantages of support vector machines include:
- If the number of features is much greater than the number of samples, avoiding over-fitting via choosing kernel functions and regularization term is crucial.
- SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation (see Scores and probabilities, below).
from sklearn.svm import LinearSVC
from sklearn.pipeline import Pipeline
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', LinearSVC()),
])
text_clf.fit(X_train, y_train)
predicted = text_clf.predict(X_test)
print(metrics.classification_report(y_test, predicted))
output:
:
precision recall f1-score support
0 0.82 0.80 0.81 319
1 0.76 0.80 0.78 389
2 0.77 0.73 0.75 394
3 0.71 0.76 0.74 392
4 0.84 0.86 0.85 385
5 0.87 0.76 0.81 395
6 0.83 0.91 0.87 390
7 0.92 0.91 0.91 396
8 0.95 0.95 0.95 398
9 0.92 0.95 0.93 397
10 0.96 0.98 0.97 399
11 0.93 0.94 0.93 396
12 0.81 0.79 0.80 393
13 0.90 0.87 0.88 396
14 0.90 0.93 0.92 394
15 0.84 0.93 0.88 398
16 0.75 0.92 0.82 364
17 0.97 0.89 0.93 376
18 0.82 0.62 0.71 310
19 0.75 0.61 0.68 251
avg / total 0.85 0.85 0.85 7532
Decision Tree
One off th earlier classification algorithmm for text and data mining is adecision tree. Decision tree classifiers (DTC's) are used successfully in many diverse areas of classification. The structure of this technique includes a hierarchical decomposition of the data space (only train dataset). Decision tree as aclassification task was introduced by D. Morgan and developed by JR. Quinlan. The main idea is creating trees based on the attributes of the data points, but the challenge is determining which attribute should be inn th parent level and which one should be inn th child level. To solve this problem, De Mantaras introduced statistical modeling for feature selection in tthe ree.
.
from sklearn import tree
from sklearn.pipeline import Pipeline
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', tree.DecisionTreeClassifier()),
])
text_clf.fit(X_train, y_train)
predicted = text_clf.predict(X_test)
print(metrics.classification_report(y_test, predicted))
output:
:
precision recall f1-score support
0 0.51 0.48 0.49 319
1 0.42 0.42 0.42 389
2 0.51 0.56 0.53 394
3 0.46 0.42 0.44 392
4 0.50 0.56 0.53 385
5 0.50 0.47 0.48 395
6 0.66 0.73 0.69 390
7 0.60 0.59 0.59 396
8 0.66 0.72 0.69 398
9 0.53 0.55 0.54 397
10 0.68 0.66 0.67 399
11 0.73 0.69 0.71 396
12 0.34 0.33 0.33 393
13 0.52 0.42 0.46 396
14 0.65 0.62 0.63 394
15 0.68 0.72 0.70 398
16 0.49 0.62 0.55 364
17 0.78 0.60 0.68 376
18 0.38 0.38 0.38 310
19 0.32 0.32 0.32 251
avg / total 0.55 0.55 0.55 7532
Random Forest
Random forests or random decision forests technique is an ensemble learning method for text classification. This method was introduced by T. Kam Ho in 1995 for first time which used t trees in parallel. This technique was later developed by L. Breiman in 1999 that they found converged for RF as a margin measure.
.
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
text_clf = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', RandomForestClassifier(n_estimators=100)),
])
text_clf.fit(X_train, y_train)
predicted = text_clf.predict(X_test)
print(metrics.classification_report(y_test, predicted))
output:
:
precision recall f1-score support
0 0.69 0.63 0.66 319
1 0.56 0.69 0.62 389
2 0.67 0.78 0.72 394
3 0.67 0.67 0.67 392
4 0.71 0.78 0.74 385
5 0.78 0.68 0.73 395
6 0.74 0.92 0.82 390
7 0.81 0.79 0.80 396
8 0.90 0.89 0.90 398
9 0.80 0.89 0.84 397
10 0.90 0.93 0.91 399
11 0.89 0.91 0.90 396
12 0.68 0.49 0.57 393
13 0.83 0.65 0.73 396
14 0.81 0.88 0.84 394
15 0.68 0.91 0.78 398
16 0.67 0.86 0.75 364
17 0.93 0.78 0.85 376
18 0.86 0.48 0.61 310
19 0.79 0.31 0.45 251
avg / total 0.77 0.76 0.75 7532
Conditional Random Field (CRF)
Conditional Random Field (CRF) is an undirected graphical model as shown in thefigure. CRFs state the conditional probability of a label sequence Y givee a sequence of observation X i.e. P(Y|X). CRFs can incorporate complex features of observation sequence without violating the independence assumption by modeling the conditional probability of the label sequences rather than the joint probability P(X,Y). The concept off clique which is a fully connected subgraph and clique potential are i used for computing P(X|Y). Considering one potential function for each clique of the graph, the probability of a variable configuration corresponds to the product of a series of thenon-negative potential functionn. The value computed by each potential function is equivalent to the probability of the variables in its corresponding clique taken on a particular configuration.
.
Example from Here Let’s use CoNLL 2002 data to build a NER system CoNLL2002 corpus is available in NLTK. We use Spanish data.
.
import nltk
import sklearn_crfsuite
from sklearn_crfsuite import metrics
nltk.corpus.conll2002.fileids()
train_sents = list(nltk.corpus.conll2002.iob_sents('esp.train'))
test_sents = list(nltk.corpus.conll2002.iob_sents('esp.testb'))
sklearn-crfsuite (and python-crfsuite) supports several feature formats; here we use feature dicts.
.
def word2features(sent, i):
word = sent[i][0]
postag = sent[i][1]
features = {
'bias': 1.0,
'word.lower()': word.lower(),
'word[-3:]': word[-3:],
'word[-2:]': word[-2:],
'word.isupper()': word.isupper(),
'word.istitle()': word.istitle(),
'word.isdigit()': word.isdigit(),
'postag': postag,
'postag[:2]': postag[:2],
}
if i > 0:
word1 = sent[i-1][0]
postag1 = sent[i-1][1]
features.update({
'-1:word.lower()': word1.lower(),
'-1:word.istitle()': word1.istitle(),
'-1:word.isupper()': word1.isupper(),
'-1:postag': postag1,
'-1:postag[:2]': postag1[:2],
})
else:
features['BOS'] = True
if i < len(sent)-1:
word1 = sent[i+1][0]
postag1 = sent[i+1][1]
features.update({
'+1:word.lower()': word1.lower(),
'+1:word.istitle()': word1.istitle(),
'+1:word.isupper()': word1.isupper(),
'+1:postag': postag1,
'+1:postag[:2]': postag1[:2],
})
else:
features['EOS'] = True
return features
def sent2features(sent):
return [word2features(sent, i) for i in range(len(sent))]
def sent2labels(sent):
return [label for token, postag, label in sent]
def sent2tokens(sent):
return [token for token, postag, label in sent]
X_train = [sent2features(s) for s in train_sents]
y_train = [sent2labels(s) for s in train_sents]
X_test = [sent2features(s) for s in test_sents]
y_test = [sent2labels(s) for s in test_sents]
To see all possible CRF parameters check its docstring. Here we are useing L-BFGS training algorithm (it is default) with Elastic Net (L1 + L2) regularization.
.
crf = sklearn_crfsuite.CRF(
algorithm='lbfgs',
c1=0.1,
c2=0.1,
max_iterations=100,
all_possible_transitions=True
)
crf.fit(X_train, y_train)
Evaluation
n
y_pred = crf.predict(X_test)
print(metrics.flat_classification_report(
y_test, y_pred, digits=3
))
Output:
:
precision recall f1-score support
B-LOC 0.810 0.784 0.797 1084
B-MISC 0.731 0.569 0.640 339
B-ORG 0.807 0.832 0.820 1400
B-PER 0.850 0.884 0.867 735
I-LOC 0.690 0.637 0.662 325
I-MISC 0.699 0.589 0.639 557
I-ORG 0.852 0.786 0.818 1104
I-PER 0.893 0.943 0.917 634
O 0.992 0.997 0.994 45355
avg / total 0.970 0.971 0.971 51533
Deep Learning
Deep Neural Networks
Deep Neural Networks architectures are designed to learn through multiple connectionn of layers where eachever single layer only receives aconnection from previous and provides connections only to the next layer in hidden part. The input is a connection of feature space (As discussed in Section Feature_extraction with thefirst hidden layer. For Deep Neural Networks (DNN), theinput layer could be tf-ifd, word embedding, or etc. as shown in standard DNN in Figure. The output layer houses neurons equal to the number of classes for multi-class classification and only one neuron for binary classification. But our main contribution in this paper is that we have many trained DNNs to serve different purposes. Here, we have multi-class DNNs where each learning model is generated randomly (number of nodes in each layerr as well as the number of layerss are randomly assigned). Our implementation of Deep Neural Network (DNN) is basically a discriminatively trained model that uses astandard back-propagation algorithm and sigmoid or ReLU as activation functions. The output layer for multi-class classification should use Softmax.
.
import packages:
:
from sklearn.datasets import fetch_20newsgroups
from keras.layers import Dropout, Dense
from keras.models import Sequential
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from sklearn import metrics
convert text to TF-IDF:
:
def TFIDF(X_train, X_test,MAX_NB_WORDS=75000):
vectorizer_x = TfidfVectorizer(max_features=MAX_NB_WORDS)
X_train = vectorizer_x.fit_transform(X_train).toarray()
X_test = vectorizer_x.transform(X_test).toarray()
print("tf-idf with",str(np.array(X_train).shape[1]),"features")
return (X_train,X_test)
Build a DNN Model for Text:
:
def Build_Model_DNN_Text(shape, nClasses, dropout=0.5):
"""
buildModel_DNN_Tex(shape, nClasses,dropout)
Build Deep neural networks Model for text classification
Shape is input feature space
nClasses is number of classes
"""
model = Sequential()
node = 512 # number of nodes
nLayers = 4 # number of hidden layer
model.add(Dense(node,input_dim=shape,activation='relu'))
model.add(Dropout(dropout))
for i in range(0,nLayers):
model.add(Dense(node,input_dim=node,activation='relu'))
model.add(Dropout(dropout))
model.add(Dense(nClasses, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
Load text dataset (20newsgroups):
:
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
run DNN and see our result:
:
X_train_tfidf,X_test_tfidf = TFIDF(X_train,X_test)
model_DNN = Build_Model_DNN_Text(X_train_tfidf.shape[1], 20)
model_DNN.fit(X_train_tfidf, y_train,
validation_data=(X_test_tfidf, y_test),
epochs=10,
batch_size=128,
verbose=2)
predicted = model_DNN.predict(X_test_tfidf)
print(metrics.classification_report(y_test, predicted))
Model summary:
:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense_1 (Dense) (None, 512) 38400512
_________________________________________________________________
dropout_1 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 512) 262656
_________________________________________________________________
dropout_2 (Dropout) (None, 512) 0
_________________________________________________________________
dense_3 (Dense) (None, 512) 262656
_________________________________________________________________
dropout_3 (Dropout) (None, 512) 0
_________________________________________________________________
dense_4 (Dense) (None, 512) 262656
_________________________________________________________________
dropout_4 (Dropout) (None, 512) 0
_________________________________________________________________
dense_5 (Dense) (None, 512) 262656
_________________________________________________________________
dropout_5 (Dropout) (None, 512) 0
_________________________________________________________________
dense_6 (Dense) (None, 20) 10260
=================================================================
Total params: 39,461,396
Trainable params: 39,461,396
Non-trainable params: 0
_________________________________________________________________
Output:
:
Train on 11314 samples, validate on 7532 samples
Epoch 1/10
- 16s - loss: 2.7553 - acc: 0.1090 - val_loss: 1.9330 - val_acc: 0.3184
Epoch 2/10
- 15s - loss: 1.5330 - acc: 0.4222 - val_loss: 1.1546 - val_acc: 0.6204
Epoch 3/10
- 15s - loss: 0.7438 - acc: 0.7257 - val_loss: 0.8405 - val_acc: 0.7499
Epoch 4/10
- 15s - loss: 0.2967 - acc: 0.9020 - val_loss: 0.9214 - val_acc: 0.7767
Epoch 5/10
- 15s - loss: 0.1557 - acc: 0.9543 - val_loss: 0.8965 - val_acc: 0.7917
Epoch 6/10
- 15s - loss: 0.1015 - acc: 0.9705 - val_loss: 0.9427 - val_acc: 0.7949
Epoch 7/10
- 15s - loss: 0.0595 - acc: 0.9835 - val_loss: 0.9893 - val_acc: 0.7995
Epoch 8/10
- 15s - loss: 0.0495 - acc: 0.9866 - val_loss: 0.9512 - val_acc: 0.8079
Epoch 9/10
- 15s - loss: 0.0437 - acc: 0.9867 - val_loss: 0.9690 - val_acc: 0.8117
Epoch 10/10
- 15s - loss: 0.0443 - acc: 0.9880 - val_loss: 1.0004 - val_acc: 0.8070
precision recall f1-score support
0 0.76 0.78 0.77 319
1 0.67 0.80 0.73 389
2 0.82 0.63 0.71 394
3 0.76 0.69 0.72 392
4 0.65 0.86 0.74 385
5 0.84 0.75 0.79 395
6 0.82 0.87 0.84 390
7 0.86 0.90 0.88 396
8 0.95 0.91 0.93 398
9 0.91 0.92 0.92 397
10 0.98 0.92 0.95 399
11 0.96 0.85 0.90 396
12 0.71 0.69 0.70 393
13 0.95 0.70 0.81 396
14 0.86 0.91 0.88 394
15 0.85 0.90 0.87 398
16 0.79 0.84 0.81 364
17 0.99 0.77 0.87 376
18 0.58 0.75 0.65 310
19 0.52 0.60 0.55 251
avg / total 0.82 0.81 0.81 7532
Recurrent Neural Networks (RNN)
Another neural network architecture that is addressed by the researchers for text miming and classification is Recurrent Neural Networks (RNN). RNN assigns more weights to the previous data points of sequence. Therefore, this technique is a powerful method for text, stringg and sequential data classification. Moreover, this technique could be used for image classification as we did in this work. In RNN, the neural net considers the information of previous nodes in a very sophisticated method which allows for better semantic analysis of the structures in the dataset.
.
Gated Recurrent Unit (GRU) is a gating mechanism for RNN which was introduced by J. Chung et al. and K.Cho et al.. GRU is a simplified variant of the LSTM architecture, but there are differences as follows: GRU contains two gates and does not possess any internal memory (as shown in Figure; and finally, a second non-linearity is not applied (tanh in Figure).
.
Long Short-Term Memory~(LSTM) was introduced by S. Hochreiter and J. Schmidhuber and developed by many research scientists.
.To deal with these problems Long Short-Term Memory (LSTM) is a special type of RNN that preserves long term dependency in a more effective way compared to the basic RNNs. This is particularly useful to overcome vanishing gradient problem. Although LSTM has a chain-like structure similar to RNN, LSTM uses multiple gates to carefully regulate the amount of information that will be allowed into each node state. F The igure shows the basic cell of aa LSTM model.
.import packages:
:
from keras.layers import Dropout, Dense, GRU, Embedding
from keras.models import Sequential
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from sklearn import metrics
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.datasets import fetch_20newsgroups
convert text to word embedding (Using GloVe):
:
def loadData_Tokenizer(X_train, X_test,MAX_NB_WORDS=75000,MAX_SEQUENCE_LENGTH=500):
np.random.seed(7)
text = np.concatenate((X_train, X_test), axis=0)
text = np.array(text)
tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(text)
sequences = tokenizer.texts_to_sequences(text)
word_index = tokenizer.word_index
text = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
print('Found %s unique tokens.' % len(word_index))
indices = np.arange(text.shape[0])
# np.random.shuffle(indices)
text = text[indices]
print(text.shape)
X_train = text[0:len(X_train), ]
X_test = text[len(X_train):, ]
embeddings_index = {}
f = open("C:\\Users\\kamran\\Documents\\GitHub\\RMDL\\Examples\\Glove\\glove.6B.50d.txt", encoding="utf8")
for line in f:
values = line.split()
word = values[0]
try:
coefs = np.asarray(values[1:], dtype='float32')
except:
pass
embeddings_index[word] = coefs
f.close()
print('Total %s word vectors.' % len(embeddings_index))
return (X_train, X_test, word_index,embeddings_index)
Build aa RNN Model for Text:
:
def Build_Model_RNN_Text(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):
"""
def buildModel_RNN(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):
word_index in word index ,
embeddings_index is embeddings index, look at data_helper.py
nClasses is number of classes,
MAX_SEQUENCE_LENGTH is maximum lenght of text sequences
"""
model = Sequential()
hidden_layer = 3
gru_node = 32
embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
if len(embedding_matrix[i]) != len(embedding_vector):
print("could not broadcast input array from shape", str(len(embedding_matrix[i])),
"into shape", str(len(embedding_vector)), " Please make sure your"
" EMBEDDING_DIM is equal to embedding_vector file ,GloVe,")
exit(1)
embedding_matrix[i] = embedding_vector
model.add(Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=True))
print(gru_node)
for i in range(0,hidden_layer):
model.add(GRU(gru_node,return_sequences=True, recurrent_dropout=0.2))
model.add(Dropout(dropout))
model.add(GRU(gru_node, recurrent_dropout=0.2))
model.add(Dropout(dropout))
model.add(Dense(256, activation='relu'))
model.add(Dense(nclasses, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
run RNN and see our result:
:
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
X_train_Glove,X_test_Glove, word_index,embeddings_index = loadData_Tokenizer(X_train,X_test)
model_RNN = Build_Model_RNN_Text(word_index,embeddings_index, 20)
model_RNN.fit(X_train_Glove, y_train,
validation_data=(X_test_Glove, y_test),
epochs=10,
batch_size=128,
verbose=2)
predicted = model_RNN.predict_classes(X_test_Glove)
print(metrics.classification_report(y_test, predicted))
Model summary:
:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, 500, 50) 8960500
_________________________________________________________________
gru_1 (GRU) (None, 500, 256) 235776
_________________________________________________________________
dropout_1 (Dropout) (None, 500, 256) 0
_________________________________________________________________
gru_2 (GRU) (None, 500, 256) 393984
_________________________________________________________________
dropout_2 (Dropout) (None, 500, 256) 0
_________________________________________________________________
gru_3 (GRU) (None, 500, 256) 393984
_________________________________________________________________
dropout_3 (Dropout) (None, 500, 256) 0
_________________________________________________________________
gru_4 (GRU) (None, 256) 393984
_________________________________________________________________
dense_1 (Dense) (None, 20) 5140
=================================================================
Total params: 10,383,368
Trainable params: 10,383,368
Non-trainable params: 0
_________________________________________________________________
Output:
:
Train on 11314 samples, validate on 7532 samples
Epoch 1/20
- 268s - loss: 2.5347 - acc: 0.1792 - val_loss: 2.2857 - val_acc: 0.2460
Epoch 2/20
- 271s - loss: 1.6751 - acc: 0.3999 - val_loss: 1.4972 - val_acc: 0.4660
Epoch 3/20
- 270s - loss: 1.0945 - acc: 0.6072 - val_loss: 1.3232 - val_acc: 0.5483
Epoch 4/20
- 269s - loss: 0.7761 - acc: 0.7312 - val_loss: 1.1009 - val_acc: 0.6452
Epoch 5/20
- 269s - loss: 0.5513 - acc: 0.8112 - val_loss: 1.0395 - val_acc: 0.6832
Epoch 6/20
- 269s - loss: 0.3765 - acc: 0.8754 - val_loss: 0.9977 - val_acc: 0.7086
Epoch 7/20
- 270s - loss: 0.2481 - acc: 0.9202 - val_loss: 1.0485 - val_acc: 0.7270
Epoch 8/20
- 269s - loss: 0.1717 - acc: 0.9463 - val_loss: 1.0269 - val_acc: 0.7394
Epoch 9/20
- 269s - loss: 0.1130 - acc: 0.9644 - val_loss: 1.1498 - val_acc: 0.7369
Epoch 10/20
- 269s - loss: 0.0640 - acc: 0.9808 - val_loss: 1.1442 - val_acc: 0.7508
Epoch 11/20
- 269s - loss: 0.0567 - acc: 0.9828 - val_loss: 1.2318 - val_acc: 0.7414
Epoch 12/20
- 268s - loss: 0.0472 - acc: 0.9858 - val_loss: 1.2204 - val_acc: 0.7496
Epoch 13/20
- 269s - loss: 0.0319 - acc: 0.9910 - val_loss: 1.1895 - val_acc: 0.7657
Epoch 14/20
- 268s - loss: 0.0466 - acc: 0.9853 - val_loss: 1.2821 - val_acc: 0.7517
Epoch 15/20
- 271s - loss: 0.0269 - acc: 0.9917 - val_loss: 1.2869 - val_acc: 0.7557
Epoch 16/20
- 271s - loss: 0.0187 - acc: 0.9950 - val_loss: 1.3037 - val_acc: 0.7598
Epoch 17/20
- 268s - loss: 0.0157 - acc: 0.9959 - val_loss: 1.2974 - val_acc: 0.7638
Epoch 18/20
- 270s - loss: 0.0121 - acc: 0.9966 - val_loss: 1.3526 - val_acc: 0.7602
Epoch 19/20
- 269s - loss: 0.0262 - acc: 0.9926 - val_loss: 1.4182 - val_acc: 0.7517
Epoch 20/20
- 269s - loss: 0.0249 - acc: 0.9918 - val_loss: 1.3453 - val_acc: 0.7638
precision recall f1-score support
0 0.71 0.71 0.71 319
1 0.72 0.68 0.70 389
2 0.76 0.62 0.69 394
3 0.67 0.58 0.62 392
4 0.68 0.67 0.68 385
5 0.75 0.73 0.74 395
6 0.82 0.74 0.78 390
7 0.83 0.83 0.83 396
8 0.81 0.90 0.86 398
9 0.92 0.90 0.91 397
10 0.91 0.94 0.93 399
11 0.87 0.76 0.81 396
12 0.57 0.70 0.63 393
13 0.81 0.85 0.83 396
14 0.74 0.93 0.82 394
15 0.82 0.83 0.83 398
16 0.74 0.78 0.76 364
17 0.96 0.83 0.89 376
18 0.64 0.60 0.62 310
19 0.48 0.56 0.52 251
avg / total 0.77 0.76 0.76 7532
Convolutional Neural Networks (CNN)
Another deep learning architecture that is employed for hierarchical document classification is Convolutional Neural Networks (CNN) . Although originally built for image processing with architecture similar to the visual cortex, CNNs have also been effectively used for text classification. In a basic CNN for image processing, an image tensor is convolved with a set of kernels of size d by d. These convolution layers are called feature maps and can be stacked to provide multiple filters on the input. To reduce the computational complexity, CNNs use pooling which reduces the size of the output from one layer to the next in the network. Different pooling techniques are used to reduce outputs while preserving important features.
.The most common pooling method is max pooling where the maximum element is selected from the pooling window. In order to feed the pooled output from stacked featured maps to the next layer, the maps are flattened into one column. The final layers in a CNN are typically fully connected dense layers. In general, during the back-propagation step of a convolutional neural network not only the weights are adjusted but also the feature detector filters. A potential problem of CNN used for text is the number of 'channels', Sigma (size of the feature space). This might be very large (e.g. 50K), for text but for images this is less of a problem (e.g. only 3 channels of RGB). This means the dimensionality of the CNN for text is very high.
.
import packages:
:
from keras.layers import Dropout, Dense,Input,Embedding,Flatten, MaxPooling1D, Conv1D
from keras.models import Sequential,Model
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from sklearn import metrics
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.datasets import fetch_20newsgroups
from keras.layers.merge import Concatenate
convert text to word embedding (Using GloVe):
:
def loadData_Tokenizer(X_train, X_test,MAX_NB_WORDS=75000,MAX_SEQUENCE_LENGTH=500):
np.random.seed(7)
text = np.concatenate((X_train, X_test), axis=0)
text = np.array(text)
tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(text)
sequences = tokenizer.texts_to_sequences(text)
word_index = tokenizer.word_index
text = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
print('Found %s unique tokens.' % len(word_index))
indices = np.arange(text.shape[0])
# np.random.shuffle(indices)
text = text[indices]
print(text.shape)
X_train = text[0:len(X_train), ]
X_test = text[len(X_train):, ]
embeddings_index = {}
f = open("C:\\Users\\kamran\\Documents\\GitHub\\RMDL\\Examples\\Glove\\glove.6B.50d.txt", encoding="utf8")
for line in f:
values = line.split()
word = values[0]
try:
coefs = np.asarray(values[1:], dtype='float32')
except:
pass
embeddings_index[word] = coefs
f.close()
print('Total %s word vectors.' % len(embeddings_index))
return (X_train, X_test, word_index,embeddings_index)
Build a CNN Model for Text:
:
def Build_Model_CNN_Text(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):
"""
def buildModel_CNN(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50, dropout=0.5):
word_index in word index ,
embeddings_index is embeddings index, look at data_helper.py
nClasses is number of classes,
MAX_SEQUENCE_LENGTH is maximum lenght of text sequences,
EMBEDDING_DIM is an int value for dimention of word embedding look at data_helper.py
"""
model = Sequential()
embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
if len(embedding_matrix[i]) !=len(embedding_vector):
print("could not broadcast input array from shape",str(len(embedding_matrix[i])),
"into shape",str(len(embedding_vector))," Please make sure your"
" EMBEDDING_DIM is equal to embedding_vector file ,GloVe,")
exit(1)
embedding_matrix[i] = embedding_vector
embedding_layer = Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=True)
# applying a more complex convolutional approach
convs = []
filter_sizes = []
layer = 5
print("Filter ",layer)
for fl in range(0,layer):
filter_sizes.append((fl+2))
node = 128
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
for fsz in filter_sizes:
l_conv = Conv1D(node, kernel_size=fsz, activation='relu')(embedded_sequences)
l_pool = MaxPooling1D(5)(l_conv)
#l_pool = Dropout(0.25)(l_pool)
convs.append(l_pool)
l_merge = Concatenate(axis=1)(convs)
l_cov1 = Conv1D(node, 5, activation='relu')(l_merge)
l_cov1 = Dropout(dropout)(l_cov1)
l_pool1 = MaxPooling1D(5)(l_cov1)
l_cov2 = Conv1D(node, 5, activation='relu')(l_pool1)
l_cov2 = Dropout(dropout)(l_cov2)
l_pool2 = MaxPooling1D(30)(l_cov2)
l_flat = Flatten()(l_pool2)
l_dense = Dense(1024, activation='relu')(l_flat)
l_dense = Dropout(dropout)(l_dense)
l_dense = Dense(512, activation='relu')(l_dense)
l_dense = Dropout(dropout)(l_dense)
preds = Dense(nclasses, activation='softmax')(l_dense)
model = Model(sequence_input, preds)
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
run CNN and see our result:
:
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
X_train_Glove,X_test_Glove, word_index,embeddings_index = loadData_Tokenizer(X_train,X_test)
model_CNN = Build_Model_CNN_Text(word_index,embeddings_index, 20)
model_CNN.summary()
model_CNN.fit(X_train_Glove, y_train,
validation_data=(X_test_Glove, y_test),
epochs=15,
batch_size=128,
verbose=2)
predicted = model_CNN.predict(X_test_Glove)
predicted = np.argmax(predicted, axis=1)
print(metrics.classification_report(y_test, predicted))
Model:
:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 500) 0
__________________________________________________________________________________________________
embedding_1 (Embedding) (None, 500, 50) 8960500 input_1[0][0]
__________________________________________________________________________________________________
conv1d_1 (Conv1D) (None, 499, 128) 12928 embedding_1[0][0]
__________________________________________________________________________________________________
conv1d_2 (Conv1D) (None, 498, 128) 19328 embedding_1[0][0]
__________________________________________________________________________________________________
conv1d_3 (Conv1D) (None, 497, 128) 25728 embedding_1[0][0]
__________________________________________________________________________________________________
conv1d_4 (Conv1D) (None, 496, 128) 32128 embedding_1[0][0]
__________________________________________________________________________________________________
conv1d_5 (Conv1D) (None, 495, 128) 38528 embedding_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_1 (MaxPooling1D) (None, 99, 128) 0 conv1d_1[0][0]
__________________________________________________________________________________________________
max_pooling1d_2 (MaxPooling1D) (None, 99, 128) 0 conv1d_2[0][0]
__________________________________________________________________________________________________
max_pooling1d_3 (MaxPooling1D) (None, 99, 128) 0 conv1d_3[0][0]
__________________________________________________________________________________________________
max_pooling1d_4 (MaxPooling1D) (None, 99, 128) 0 conv1d_4[0][0]
__________________________________________________________________________________________________
max_pooling1d_5 (MaxPooling1D) (None, 99, 128) 0 conv1d_5[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 495, 128) 0 max_pooling1d_1[0][0]
max_pooling1d_2[0][0]
max_pooling1d_3[0][0]
max_pooling1d_4[0][0]
max_pooling1d_5[0][0]
__________________________________________________________________________________________________
conv1d_6 (Conv1D) (None, 491, 128) 82048 concatenate_1[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 491, 128) 0 conv1d_6[0][0]
__________________________________________________________________________________________________
max_pooling1d_6 (MaxPooling1D) (None, 98, 128) 0 dropout_1[0][0]
__________________________________________________________________________________________________
conv1d_7 (Conv1D) (None, 94, 128) 82048 max_pooling1d_6[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 94, 128) 0 conv1d_7[0][0]
__________________________________________________________________________________________________
max_pooling1d_7 (MaxPooling1D) (None, 3, 128) 0 dropout_2[0][0]
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 384) 0 max_pooling1d_7[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 1024) 394240 flatten_1[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 1024) 0 dense_1[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (None, 512) 524800 dropout_3[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 512) 0 dense_2[0][0]
__________________________________________________________________________________________________
dense_3 (Dense) (None, 20) 10260 dropout_4[0][0]
==================================================================================================
Total params: 10,182,536
Trainable params: 10,182,536
Non-trainable params: 0
__________________________________________________________________________________________________
Output:
:
Train on 11314 samples, validate on 7532 samples
Epoch 1/15
- 6s - loss: 2.9329 - acc: 0.0783 - val_loss: 2.7628 - val_acc: 0.1403
Epoch 2/15
- 4s - loss: 2.2534 - acc: 0.2249 - val_loss: 2.1715 - val_acc: 0.4007
Epoch 3/15
- 4s - loss: 1.5643 - acc: 0.4326 - val_loss: 1.7846 - val_acc: 0.5052
Epoch 4/15
- 4s - loss: 1.1771 - acc: 0.5662 - val_loss: 1.4949 - val_acc: 0.6131
Epoch 5/15
- 4s - loss: 0.8880 - acc: 0.6797 - val_loss: 1.3629 - val_acc: 0.6256
Epoch 6/15
- 4s - loss: 0.6990 - acc: 0.7569 - val_loss: 1.2013 - val_acc: 0.6624
Epoch 7/15
- 4s - loss: 0.5037 - acc: 0.8200 - val_loss: 1.0674 - val_acc: 0.6807
Epoch 8/15
- 4s - loss: 0.4050 - acc: 0.8626 - val_loss: 1.0223 - val_acc: 0.6863
Epoch 9/15
- 4s - loss: 0.2952 - acc: 0.8968 - val_loss: 0.9045 - val_acc: 0.7120
Epoch 10/15
- 4s - loss: 0.2314 - acc: 0.9217 - val_loss: 0.8574 - val_acc: 0.7326
Epoch 11/15
- 4s - loss: 0.1778 - acc: 0.9436 - val_loss: 0.8752 - val_acc: 0.7270
Epoch 12/15
- 4s - loss: 0.1475 - acc: 0.9524 - val_loss: 0.8299 - val_acc: 0.7355
Epoch 13/15
- 4s - loss: 0.1089 - acc: 0.9657 - val_loss: 0.8034 - val_acc: 0.7491
Epoch 14/15
- 4s - loss: 0.1047 - acc: 0.9666 - val_loss: 0.8172 - val_acc: 0.7463
Epoch 15/15
- 4s - loss: 0.0749 - acc: 0.9774 - val_loss: 0.8511 - val_acc: 0.7313
precision recall f1-score support
0 0.75 0.61 0.67 319
1 0.63 0.74 0.68 389
2 0.74 0.54 0.62 394
3 0.49 0.76 0.60 392
4 0.60 0.70 0.64 385
5 0.79 0.57 0.66 395
6 0.73 0.76 0.74 390
7 0.83 0.74 0.78 396
8 0.86 0.88 0.87 398
9 0.95 0.78 0.86 397
10 0.93 0.93 0.93 399
11 0.92 0.77 0.84 396
12 0.55 0.72 0.62 393
13 0.76 0.85 0.80 396
14 0.86 0.83 0.84 394
15 0.91 0.73 0.81 398
16 0.75 0.65 0.70 364
17 0.95 0.86 0.90 376
18 0.60 0.49 0.54 310
19 0.37 0.60 0.46 251
avg / total 0.76 0.73 0.74 7532
Hierarchical Attention Networks
Recurrent Convolutional Neural Networks (RCNN)
Recurrent Convolutional Neural Networks (RCNN) is also used for text classification. The main idea of this technique is capturing contextual information with the recurrent structure and constructing the representation of text using a convolutional neural network. This architecture is a combination of RNN and CNN to use theadvantages of both techniquee in a model.
.import packages:
:
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import GRU
from keras.layers import Conv1D, MaxPooling1D
from keras.datasets import imdb
from sklearn.datasets import fetch_20newsgroups
import numpy as np
from sklearn import metrics
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
Convert text to word embedding (Using GloVe):
:
def loadData_Tokenizer(X_train, X_test,MAX_NB_WORDS=75000,MAX_SEQUENCE_LENGTH=500):
np.random.seed(7)
text = np.concatenate((X_train, X_test), axis=0)
text = np.array(text)
tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(text)
sequences = tokenizer.texts_to_sequences(text)
word_index = tokenizer.word_index
text = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
print('Found %s unique tokens.' % len(word_index))
indices = np.arange(text.shape[0])
# np.random.shuffle(indices)
text = text[indices]
print(text.shape)
X_train = text[0:len(X_train), ]
X_test = text[len(X_train):, ]
embeddings_index = {}
f = open("C:\\Users\\kamran\\Documents\\GitHub\\RMDL\\Examples\\Glove\\glove.6B.50d.txt", encoding="utf8")
for line in f:
values = line.split()
word = values[0]
try:
coefs = np.asarray(values[1:], dtype='float32')
except:
pass
embeddings_index[word] = coefs
f.close()
print('Total %s word vectors.' % len(embeddings_index))
return (X_train, X_test, word_index,embeddings_index)
def Build_Model_RCNN_Text(word_index, embeddings_index, nclasses, MAX_SEQUENCE_LENGTH=500, EMBEDDING_DIM=50):
kernel_size = 2
filters = 256
pool_size = 2
gru_node = 256
embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
if len(embedding_matrix[i]) !=len(embedding_vector):
print("could not broadcast input array from shape",str(len(embedding_matrix[i])),
"into shape",str(len(embedding_vector))," Please make sure your"
" EMBEDDING_DIM is equal to embedding_vector file ,GloVe,")
exit(1)
embedding_matrix[i] = embedding_vector
model = Sequential()
model.add(Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=True))
model.add(Dropout(0.25))
model.add(Conv1D(filters, kernel_size, activation='relu'))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Conv1D(filters, kernel_size, activation='relu'))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Conv1D(filters, kernel_size, activation='relu'))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Conv1D(filters, kernel_size, activation='relu'))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(LSTM(gru_node, return_sequences=True, recurrent_dropout=0.2))
model.add(LSTM(gru_node, return_sequences=True, recurrent_dropout=0.2))
model.add(LSTM(gru_node, return_sequences=True, recurrent_dropout=0.2))
model.add(LSTM(gru_node, recurrent_dropout=0.2))
model.add(Dense(1024,activation='relu'))
model.add(Dense(nclasses))
model.add(Activation('softmax'))
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
X_train = newsgroups_train.data
X_test = newsgroups_test.data
y_train = newsgroups_train.target
y_test = newsgroups_test.target
X_train_Glove,X_test_Glove, word_index,embeddings_index = loadData_Tokenizer(X_train,X_test)
Run RCNN :
:
model_RCNN = Build_Model_CNN_Text(word_index,embeddings_index, 20)
model_RCNN.summary()
model_RCNN.fit(X_train_Glove, y_train,
validation_data=(X_test_Glove, y_test),
epochs=15,
batch_size=128,
verbose=2)
predicted = model_RCNN.predict(X_test_Glove)
predicted = np.argmax(predicted, axis=1)
print(metrics.classification_report(y_test, predicted))
summary of the model:
:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_1 (Embedding) (None, 500, 50) 8960500
_________________________________________________________________
dropout_1 (Dropout) (None, 500, 50) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 499, 256) 25856
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 249, 256) 0
_________________________________________________________________
conv1d_2 (Conv1D) (None, 248, 256) 131328
_________________________________________________________________
max_pooling1d_2 (MaxPooling1 (None, 124, 256) 0
_________________________________________________________________
conv1d_3 (Conv1D) (None, 123, 256) 131328
_________________________________________________________________
max_pooling1d_3 (MaxPooling1 (None, 61, 256) 0
_________________________________________________________________
conv1d_4 (Conv1D) (None, 60, 256) 131328
_________________________________________________________________
max_pooling1d_4 (MaxPooling1 (None, 30, 256) 0
_________________________________________________________________
lstm_1 (LSTM) (None, 30, 256) 525312
_________________________________________________________________
lstm_2 (LSTM) (None, 30, 256) 525312
_________________________________________________________________
lstm_3 (LSTM) (None, 30, 256) 525312
_________________________________________________________________
lstm_4 (LSTM) (None, 256) 525312
_________________________________________________________________
dense_1 (Dense) (None, 1024) 263168
_________________________________________________________________
dense_2 (Dense) (None, 20) 20500
_________________________________________________________________
activation_1 (Activation) (None, 20) 0
=================================================================
Total params: 11,765,256
Trainable params: 11,765,256
Non-trainable params: 0
_________________________________________________________________
Output:
:
Train on 11314 samples, validate on 7532 samples
Epoch 1/15
- 28s - loss: 2.6624 - acc: 0.1081 - val_loss: 2.3012 - val_acc: 0.1753
Epoch 2/15
- 22s - loss: 2.1142 - acc: 0.2224 - val_loss: 1.9168 - val_acc: 0.2669
Epoch 3/15
- 22s - loss: 1.7465 - acc: 0.3290 - val_loss: 1.8257 - val_acc: 0.3412
Epoch 4/15
- 22s - loss: 1.4730 - acc: 0.4356 - val_loss: 1.5433 - val_acc: 0.4436
Epoch 5/15
- 22s - loss: 1.1800 - acc: 0.5556 - val_loss: 1.2973 - val_acc: 0.5467
Epoch 6/15
- 22s - loss: 0.9910 - acc: 0.6281 - val_loss: 1.2530 - val_acc: 0.5797
Epoch 7/15
- 22s - loss: 0.8581 - acc: 0.6854 - val_loss: 1.1522 - val_acc: 0.6281
Epoch 8/15
- 22s - loss: 0.7058 - acc: 0.7428 - val_loss: 1.2385 - val_acc: 0.6033
Epoch 9/15
- 22s - loss: 0.6792 - acc: 0.7515 - val_loss: 1.0200 - val_acc: 0.6775
Epoch 10/15
- 22s - loss: 0.5782 - acc: 0.7948 - val_loss: 1.0961 - val_acc: 0.6577
Epoch 11/15
- 23s - loss: 0.4674 - acc: 0.8341 - val_loss: 1.0866 - val_acc: 0.6924
Epoch 12/15
- 23s - loss: 0.4284 - acc: 0.8512 - val_loss: 0.9880 - val_acc: 0.7096
Epoch 13/15
- 22s - loss: 0.3883 - acc: 0.8670 - val_loss: 1.0190 - val_acc: 0.7151
Epoch 14/15
- 22s - loss: 0.3334 - acc: 0.8874 - val_loss: 1.0025 - val_acc: 0.7232
Epoch 15/15
- 22s - loss: 0.2857 - acc: 0.9038 - val_loss: 1.0123 - val_acc: 0.7331
precision recall f1-score support
0 0.64 0.73 0.68 319
1 0.45 0.83 0.58 389
2 0.81 0.64 0.71 394
3 0.64 0.57 0.61 392
4 0.55 0.78 0.64 385
5 0.77 0.52 0.62 395
6 0.84 0.77 0.80 390
7 0.87 0.79 0.83 396
8 0.85 0.90 0.87 398
9 0.98 0.84 0.90 397
10 0.93 0.96 0.95 399
11 0.92 0.79 0.85 396
12 0.59 0.53 0.56 393
13 0.82 0.82 0.82 396
14 0.84 0.84 0.84 394
15 0.83 0.89 0.86 398
16 0.68 0.86 0.76 364
17 0.97 0.86 0.91 376
18 0.66 0.50 0.57 310
19 0.53 0.31 0.40 251
avg / total 0.77 0.75 0.75 7532
Random Multimodel Deep Learning (RMDL)
Referenced paper : RMDL: Random Multimodel Deep Learning for Classification
nA new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of different deep learning architectures. RDMLs can accept a variety of data as input including text, video, images, and symbols.
.
.Installation
nThere are pip and git for RMDL installation:
:Using pip
p
pip install RMDL
Using git
t
git clone --recursive https://github.com/kk7nc/RMDL.git
The primary requirements for this package are Python 3 with Tensorflow. The requirements.txt file contains a listing of the required Python packages; to install all requirements, run the following:
:
pip -r install requirements.txt
Or
r
pip3 install -r requirements.txt
Or:
:
conda install --file requirements.txt
Documentation:
:The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches are achieving better results compared to previous machine learning algorithms on tasks like image classification, natural language processing, face recognition, and etc. The success of these deep learning algorithms rely on their capacity to model complex and non-linear relationships within the data. However, finding suitable structures for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL aims to solve the problem of finding the best deep learning architecture while simultaneously improving the robustness and accuracy through ensembles of multiple deep learning architectures. In short, RMDL trains multiple models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. To create these models, each deep learning model has been constructed in a random fashion regarding the number of layers and nodes in their neural network structure. The resulting RDML model can be used in various domains such as text, video, images, and symbolic. In this Project, we describbe te RMDL model in ndepth and show the results for image and text classification as well as face recognition. For image classification, we compared our model with some of the available baselines using MNIST and CIFAR-10 datasets. Similarly, we used four datasets namely, WOS, Reuters, IMDB, and 20newsgroupp and compared our results with available baselines. Web of Science (WOS) has been collected by authors and consists of three sets~(small, mediumm and large set). Lastly, we used ORL dataset to compare the performance of our approach with other face recognition methods. These test results show that theRDML model consistently outperformm standard methods over a broad range of data types and classification problems.
Hierarchical Deep Learning for Text (HDLTex)
Reffrenced paper : HDLTex: Hierarchical Deep Learning for Text Classification
:Increasingly large document collections require improved information processing methods for searching, retrieving, and organizing text documents. Central to these information processing methods is document classification, which has become an important task supervised learning aims to solve. Recently, the performance of traditional supervised classifiers has degraded as the number of documents has increased. This exponential growth of document volume has also increated the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide hierarchical understanding of the documents.
Comparison Text Classification Algorithms
| Model | Advantages | Disadvantages |
| Rocchio Algorithm |
|
|
| Boosting and Bagging |
|
|
| Logistic Regression |
|
|
| Naive Bayes Classifier |
|
|
| K-Nearest Neighbor |
|
|
| Support Vector Machine (SVM) |
|
|
| Decision Tree |
|
|
| Conditional Random Field (CRF) |
|
|
| Random Forest |
|
|
| Deep Learning |
|
|
Evaluation
F1 Score
Matthew correlation coefficient (MCC)
Compute the Matthews correlation coefficient (MCC)
)The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary (two-class) classification problems. It takes into account of true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction. The statistic is also known as the phi coefficient.
.
from msklearn.nmetrics simport tmatthews_corrcoef
fy_true e= [= +1, 1,+1, 1,+1, 1,-1]
1]y_pred d= [= +1, 1,-1, 1,+1, 1,+1]
1]matthews_corrcoef(fy_true, e,y_pred)dReceiver operating characteristics (ROC)
ROC curves are typically used in binary classification to study the output of a classifier. In order to extend ROC curve and ROC area to multi-class or multi-label classification, it is necessary to binarize the output. One ROC curve can be drawn per label, but one can also draw a ROC curve by considering each element of the label indicator matrix as a binary prediction (micro-averaging).
.Another evaluation measure for multi-class classification is macro-averaging, which gives equal weight to the classification of each label. [sources]
]
import tnumpy yas snp
pimport tmatplotlib.bpyplot tas splt
tfrom mitertools simport tcycle
e
from msklearn nimport tsvm, m,datasets
sfrom msklearn.nmetrics simport troc_curve, e,auc
cfrom msklearn.nmodel_selection nimport ttrain_test_split
tfrom msklearn.npreprocessing gimport tlabel_binarize
efrom msklearn.nmulticlass simport tOneVsRestClassifier
rfrom mscipy yimport tinterp
p
# Import some data to play with
hiris s= =datasets.sload_iris()
s()X X= =iris.sdata
ay y= =iris.starget
t
# Binarize the output
ty y= =label_binarize(ey, y,classes=[=0, 0,1, 1,2])
2])n_classes s= =y.yshape[e1]
1]
# Add noisy features to make the problem harder
rrandom_state e= =np.prandom.mRandomState(e0)
0)n_samples, s,n_features s= =X.Xshape
eX X= =np.pc_[_X, X,random_state.erandn(nn_samples, s,200 0* *n_features)]
s)]
# shuffle and split training and test sets
sX_train, n,X_test, t,y_train, n,y_test t= =train_test_split(tX, X,y, y,test_size=.5,
5,
random_state=0)
0)
# Learn to predict each class against the other
rclassifier r= =OneVsRestClassifier(rsvm.mSVC(Ckernel='linear', ',probability=True,
e,
random_state=random_state))
e))y_score e= =classifier.rfit(tX_train, n,y_train).n)decision_function(nX_test)
t)
# Compute ROC curve and ROC area for each class
sfpr r= =dict()
t()tpr r= =dict()
t()roc_auc c= =dict()
t()for ri iin nrange(en_classes):
s):
fpr[ri], i],tpr[ri], i],_ _= =roc_curve(ey_test[:, t[:,i], i],y_score[:, e[:,i])
i])
roc_auc[ci] i]= =auc(cfpr[ri], i],tpr[ri])
i])
# Compute micro-average ROC curve and ROC area
afpr[r"micro"], "],tpr[r"micro"], "],_ _= =roc_curve(ey_test.travel(), l(),y_score.eravel())
l())roc_auc[c"micro"] "]= =auc(cfpr[r"micro"], "],tpr[r"micro"])"]Plot of a ROC curve for a specific class
s
plt.tfigure()
e()lw w= =2
2plt.tplot(tfpr[r2], 2],tpr[r2], 2],color='darkorange',
',
lw=lw, w,label='ROC curve (area = %0.2f)' '% %roc_auc[c2])
2])plt.tplot([t(0, 0,1], [1], 0, 0,1], 1],color='navy', ',lw=lw, w,linestyle='--')
')plt.txlim([m(0.0, 0,1.0])
0])plt.tylim([m(0.0, 0,1.05])
5])plt.txlabel(l'False Positive Rate')
')plt.tylabel(l'True Positive Rate')
')plt.ttitle(e'Receiver operating characteristic example')
')plt.tlegend(dloc="lower right")
")plt.tshow()w(
Area Under Curve (AUC)
Area under ROC curve (AUC) is a summary metric that measures the entire area underneath the ROC curve. AUC holds helpful properties, such as increased sensitivity in the analysis of variance (ANOVA) tests, independence of decision threshold, invariance to a priori class probability and the indication of how well negative and positive classes are regarding decision index.
.
import numpy as np
from sklearn import metrics
fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2)
metrics.auc(fpr, tpr)
Text and Document Datasets
IMDB
Dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer "3" encodes the 3rd most frequent word in the data. This allows for quick filtering operations, such as "only consider the top 10,000 most common words, but eliminate the top 20 most common words".
.As a convention, "0" does not stand for a specific word, but instead is used to encode any unknown word.
.
from keras.datasets import imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data(path="imdb.npz",
num_words=None,
skip_top=0,
maxlen=None,
seed=113,
start_char=1,
oov_char=2,
index_from=3)
Reuters-21578
Dataset of 11,228 newswires from Reuters, labeled over 46 topics. As with the IMDB dataset, each wire is encoded as a sequence of word indexes (same conventions).
.
from keras.datasets import reuters
(x_train, y_train), (x_test, y_test) = reuters.load_data(path="reuters.npz",
num_words=None,
skip_top=0,
maxlen=None,
test_split=0.2,
seed=113,
start_char=1,
oov_char=2,
index_from=3)
20Newsgroups
The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). The split between the train and test set is based upon messages posted before and after a specific date.
.This module contains two loaders. The first one, sklearn.datasets.fetch_20newsgroups, returns a list of the raw texts that can be fed to text feature extractors, such as sklearn.feature_extraction.text.CountVectorizer with custom parameters so as to extract feature vectors. The second one, sklearn.datasets.fetch_20newsgroups_vectorized, returns ready-to-use features, i.e., it is not necessary to use a feature extractor.
.
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
from pprint import pprint
pprint(list(newsgroups_train.target_names))
['alt.atheism',
'comp.graphics',
'comp.os.ms-windows.misc',
'comp.sys.ibm.pc.hardware',
'comp.sys.mac.hardware',
'comp.windows.x',
'misc.forsale',
'rec.autos',
'rec.motorcycles',
'rec.sport.baseball',
'rec.sport.hockey',
'sci.crypt',
'sci.electronics',
'sci.med',
'sci.space',
'soc.religion.christian',
'talk.politics.guns',
'talk.politics.mideast',
'talk.politics.misc',
'talk.religion.misc']
Web of Science Dataset
Description of Dataset:
:Here are i three datasets which include WOS-119677, WOS-46985, and WOS-5736 Each folder contains:
:
- X.txt
- Y.txt
- YL1.txt
- YL2.txt
X is input data that include text sequences Y is target value YL1 is the target value of level one (parent label) YL2 is the target value of level one (child label)
)Meta-data: This folder contains on data file as the following attribute: Y1 Y2 Y Domain area keywords Abstract
The at
bstract is input data that include text sequences of 46,985 published paper Y is target value YL1 is the target value of level one (parent label) YL2 is the target value of level one (child label) Domain is the maj major domain which includes 7 labelbals: {Computer Science, Electrical Engineering, Psychology, Mechanical Engineering, Civil Engineering, Medical Science, biochemistry} area is subdomain or area of the paper, such as CS-> computer graphics which contain 134 labels. keywordss: is authors keyword of the papers
s
- Web of Science Dataset WOS-11967
This dataset contains 11,967 documents with 35 categories which include 7 parents categories.
.
- Web of Science Dataset WOS-46985
This dataset contains 46,985 documents with 134 categories which include 7 parents categories.
.
- Web of Science Dataset WOS-5736
This dataset contains 5,736 documents with 11 categories which include 3 parents categories.
.
Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classificationn
@ARTICLE{Kowsari2018Text_Classification,
title={Text Classification Algorithms: A Survey},
author={Kowsari, Kamran and Jafari Meimandi, Kiana and Heidarysafa, Mojtaba and Mendu, Sanjana and Barnes, Laura E. and Brown, Donald E.},
journal={Information},
VOLUME = {10},
YEAR = {2019},
NUMBER = {4},
ARTICLE-NUMBER = {150},
URL = {http://www.mdpi.com/2078-2489/10/4/150},
ISSN = {2078-2489},
publisher={Multidisciplinary Digital Publishing Institute}
}
