Text Classification

the purpose of this repository is to explore text classification methods in NLP with deep learning.

UPDATE:

1. Apply AI in law cases task(AI_LAW): Predict the name of crimes(accusations), relevant-articles given facts of law cases, has been released

2. sentence similarity project has been released you can check it if you like.

3. if you want to try a model now, you can go to folder 'a02_TextCNN', run 'python -u p7_TextCNN_train.py', it will use sample data to train a model, and print loss and F1 score periodically.

it has all kinds of baseline models for text classificaiton.

it also support for multi-label classification where multi label associate with an sentence or document.

although many of these models are simple, and may not get you to top level of the task.but some of these models are very classic, so they may be good to serve as baseline models.

each model has a test function under model class. you can run it to performance toy task first. the model is indenpendent from dataset.

check here for formal report of large scale multi-label text classification with deep learning

serveral modes here can also be used for modelling question answering (with or without context), or to do sequences generating.

we explore two seq2seq model(seq2seq with attention,transformer-attention is all you need) to do text classification. and these two models can also be used for sequences generating and other tasks. if you task is a multi-label classification, you can cast the problem to sequences generating.

we implement two memory network. one is dynamic memory network. previously it reached state of art in question answering, sentiment analysis and sequence generating tasks. it is so called one model to do serveral different tasks, and reach high performance. it has four modules. the key component is episodic memory module. it use gate mechanism to performance attention, and use gated-gru to update episode memory, then it has another gru( in a vertical direction) to pefromance hidden state update. it has ability to do transitive inference.

the second memory network we implemented is recurrent entity network: tracking state of the world. it has blocks of key-value pairs as memory, run in parallel, which achieve new state of art. it can be used for modelling question answering with contexts(or history). for example, you can let the model to read some sentences(as context), and ask a question(as query), then ask the model to predict an answer; if you feed story same as query, then it can do classification task.

if you need some sample data and word embedding pertrained on word2vec, you can find it in closed issues, such as:issue 3.

you can also find some sample data at folder "data". it contains two files:'sample_single_label.txt', contains 50k data with single label; 'sample_multiple_label.txt', contains 20k data with multiple labels. input and label of is separate by " label".

if you want to know more detail about dataset of text classification or task these models can be used, one of choose is below: https://biendata.com/competition/zhihu/

Models:

1. fastText

2. TextCNN

3. TextRNN

4. RCNN

5. Hierarchical Attention Network

6. seq2seq with attention

7. Transformer("Attend Is All You Need")

8. Dynamic Memory Network

9. EntityNetwork:tracking state of the world

10. Ensemble models

11. Boosting:

    for a single model, stack identical models together. each layer is a model. the result will be based on logits added together. the only connection between layers are label's weights. the front layer's prediction error rate of each label will become weight for the next layers. those labels with high error rate will have big weight. so later layer's will pay more attention to those mis-predicted labels, and try to fix previous mistake of former layer. as a result, we will get a much strong model. check a00_boosting/boosting.py

and other models:

1. BiLstmTextRelation;

2. twoCNNTextRelation;

3. BiLstmTextRelationTwoRNN

Performance

(mulit-label label prediction task,ask to prediction top5, 3 million training data,full score:0.5)

Model	fastText	TextCNN	TextRNN	RCNN	HierAtteNet	Seq2seqAttn	EntityNet	DynamicMemory	Transformer
Score	0.362	0.405	0.358	0.395	0.398	0.322	0.400	0.392	0.322
Training	10m	2h	10h	2h	2h	3h	3h	5h	7h

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Ensemble of TextCNN,EntityNet,DynamicMemory: 0.411

Ensemble EntityNet,DynamicMemory: 0.403

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Notice:

m stand for minutes; h stand for hours;

HierAtteNet means Hierarchical Attention Networkk;

Seq2seqAttn means Seq2seq with attention;

DynamicMemory means DynamicMemoryNetwork;

Transformer stand for model from 'Attention Is All You Need'.

Useage:

1. model is in xxx_model.py
2. run python xxx_train.py to train the model
3. run python xxx_predict.py to do inference(test).

Each model has a test method under the model class. you can run the test method first to check whether the model can work properly.

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Environment:

python 2.7+ tensorflow 1.1

(tensorflow 1.2,1.3,1.4 also works; most of models should also work fine in other tensorflow version, since we use very few features bond to certain version; if you use python 3.5, it will be fine as long as you change print/try catch function)

TextCNN model is already transfomed to python 3.6

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Notice:

Some util function is in data_util.py; typical input like: "x1 x2 x3 x4 x5 label 323434" where 'x1,x2' is words, '323434' is label; it has a function to load and assign pretrained word embedding to the model,where word embedding is pretrained in word2vec or fastText.

Models Detail:

1.fastText:

implmentation of Bag of Tricks for Efficient Text Classification

after embed each word in the sentence, this word representations are then averaged into a text representation, which is in turn fed to a linear classifier.it use softmax function to compute the probability distribution over the predefined classes. then cross entropy is used to compute loss. bag of word representation does not consider word order. in order to take account of word order, n-gram features is used to capture some partial information about the local word order; when the number of classes is large, computing the linear classifier is computational expensive. so it usehierarchical softmax to speed training process.

1. use bi-gram and/or tri-gram
2. use NCE loss to speed us softmax computation(not use hierarchy softmax as original paper)

result: performance is as good as paper, speed also very fast.

check: p5_fastTextB_model.py

2.TextCNN:

Implementation of Convolutional Neural Networks for Sentence Classification

Structure:embedding--->conv--->max pooling--->fully connected layer-------->softmax

Check: p7_TextCNN_model.py

In order to get very good result with TextCNN, you also need to read carefully about this paper A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification: it give you some insights of things that can affect performance. although you need to change some settings according to your specific task.

Convolutional Neural Network is main building box for solve problems of computer vision. Now we will show how CNN can be used for NLP, in in particular, text classification. Sentence length will be different from one to another. So we will use pad to get fixed length, n. For each token in the sentence, we will use word embedding to get a fixed dimension vector, d. So our input is a 2-dimension matrix:(n,d). This is similar with image for CNN.

Firstly, we will do convolutional operation to our input. It is a element-wise multiply between filter and part of input. We use k number of filters, each filter size is a 2-dimension matrix (f,d). Now the output will be k number of lists. Each list has a length of n-f+1. each element is a scalar. Notice that the second dimension will be always the dimension of word embedding. We are using different size of filters to get rich features from text inputs. And this is something similar with n-gram features.

Secondly, we will do max pooling for the output of convolutional operation. For k number of lists, we will get k number of scalars.

Thirdly, we will concatenate scalars to form final features. It is a fixed-size vector. And it is independent from the size of filters we use.

Finally, we will use linear layer to project these features to per-defined labels.

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3.TextRNN

Structure v1:embedding--->bi-directional lstm--->concat output--->average----->softmax layer

check: p8_TextRNN_model.py

Structure v2:embedding-->bi-directional lstm---->dropout-->concat ouput--->lstm--->droput-->FC layer-->softmax layer

check: p8_TextRNN_model_multilayer.py

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4.BiLstmTextRelation

Structure same as TextRNN. but input is special designed. e.g.input:"how much is the computer? EOS price of laptop". where 'EOS' is a special token spilted question1 and question2.

check:p9_BiLstmTextRelation_model.py

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5.twoCNNTextRelation

Structure: first use two different convolutional to extract feature of two sentences. then concat two features. use linear transform layer to out projection to target label, then softmax.

check: p9_twoCNNTextRelation_model.py

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6.BiLstmTextRelationTwoRNN

Structure: one bi-directional lstm for one sentence(get output1), another bi-directional lstm for another sentence(get output2). then: softmax(output1Moutput2)

check:p9_BiLstmTextRelationTwoRNN_model.py

for more detail you can go to: Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow

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7.RCNN:

Recurrent convolutional neural network for text classification

implementation of Recurrent Convolutional Neural Network for Text Classification

structure:1)recurrent structure (convolutional layer) 2)max pooling 3) fully connected layer+softmax

it learn represenation of each word in the sentence or document with left side context and right side context:

representation current word=[left_side_context_vector,current_word_embedding,right_side_context_vecotor].

for left side context, it use a recurrent structure, a no-linearity transfrom of previous word and left side previous context; similarly to right side context.

check: p71_TextRCNN_model.py

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8.Hierarchical Attention Network:

Implementation of Hierarchical Attention Networks for Document Classification

Structure:

1. embedding

2. Word Encoder: word level bi-directional GRU to get rich representation of words

3. Word Attention:word level attention to get important information in a sentence

4. Sentence Encoder: sentence level bi-directional GRU to get rich representation of sentences

5. Sentence Attetion: sentence level attention to get important sentence among sentences

6. FC+Softmax

In NLP, text classification can be done for single sentence, but it can also be used for multiple sentences. we may call it document classification. Words are form to sentence. And sentence are form to document. In this circumstance, there may exists a intrinsic structure. So how can we model this kinds of task? Does all parts of document are equally relevant? And how we determine which part are more important than another?

It has two unique features:

1)it has a hierarchical structure that reflect the hierarchical structure of documents;

2)it has two levels of attention mechanisms used at the word and sentence-level. it enable the model to capture important information in different levels.

Word Encoder: For each words in a sentence, it is embedded into word vector in distribution vector space. It use a bidirectional GRU to encode the sentence. By concatenate vector from two direction, it now can form a representation of the sentence, which also capture contextual information.

Word Attention: Same words are more important than another for the sentence. So attention mechanism is used. It first use one layer MLP to get uit hidden representation of the sentence, then measure the importance of the word as the similarity of uit with a word level context vector uw and get a normalized importance through a softmax function.

Sentence Encoder: for sentence vectors, bidirectional GRU is used to encode it. Similarly to word encoder.

Sentence Attention: sentence level vector is used to measure importance among sentences. Similarly to word attention.

Input of data:

Generally speaking, input of this model should have serveral sentences instead of sinle sentence. shape is:[None,sentence_lenght]. where None means the batch_size.

In my training data, for each example, i have four parts. each part has same length. i concat four parts to form one single sentence. the model will split the sentence into four parts, to form a tensor with shape:[None,num_sentence,sentence_length]. where num_sentence is number of sentences(equal to 4, in my setting).

check:p1_HierarchicalAttention_model.py

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9.Seq2seq with attention

Implementation seq2seq with attention derived from NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE

I.Structure:

1)embedding 2)bi-GRU too get rich representation from source sentences(forward & backward). 3)decoder with attention.

II.Input of data:

there are two kinds of three kinds of inputs:1)encoder inputs, which is a sentence; 2)decoder inputs, it is labels list with fixed length;3)target labels, it is also a list of labels.

for example, labels is:"L1 L2 L3 L4", then decoder inputs will be:[_GO,L1,L2,L2,L3,_PAD]; target label will be:[L1,L2,L3,L3,_END,_PAD]. length is fixed to 6, any exceed labels will be trancated, will pad if label is not enough to fill.

III.Attention Mechanism:

1. transfer encoder input list and hidden state of decoder

2. calculate similiarity of hidden state with each encoder input, to get possibility distribution for each encoder input.

3. weighted sum of encoder input based on possibility distribution.

   go though RNN Cell using this weight sum together with decoder input to get new hidden state

IV.How Vanilla Encoder Decoder Works:

the source sentence will be encoded using RNN as fixed size vector ("thought vector"). then during decoder:

1. when it is training, another RNN will be used to try to get a word by using this "thought vector" as init state, and take input from decoder input at each timestamp. decoder start from special token "_GO". after one step is performanced, new hidden state will be get and together with new input, we can continue this process until we reach to a special token "_END". we can calculate loss by compute cross entropy loss of logits and target label. logits is get through a projection layer for the hidden state(for output of decoder step(in GRU we can just use hidden states from decoder as output).

2. when it is testing, there is no label. so we should feed the output we get from previous timestamp, and continue the process util we reached "_END" TOKEN.

V.Notices:

1. here i use two kinds of vocabularies. one is from words,used by encoder; another is for labels,used by decoder

2. for vocabulary of lables, i insert three special token:"_GO","_END","_PAD"; "_UNK" is not used, since all labels is pre-defined.

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10.Transformer("Attention Is All You Need")

Status: it was able to do task classification. and able to generate reverse order of its sequences in toy task. you can check it by running test function in the model. check: a2_train_classification.py(train) or a2_transformer_classification.py(model)

we do it in parallell style.layer normalization,residual connection, and mask are also used in the model.

For every building blocks, we include a test function in the each file below, and we've test each small piece successfully.

Sequence to sequence with attention is a typical model to solve sequence generation problem, such as translate, dialogue system. most of time, it use RNN as buidling block to do these tasks. util recently, people also apply convolutional Neural Network for sequence to sequence problem. Transformer, however, it perform these tasks solely on attention mechansim. it is fast and acheive new state-of-art result.

It also has two main parts: encoder and decoder. below is desc from paper:

Encoder:

6 layers.each layers has two sub-layers. the first is multi-head self-attention mechanism; the second is position-wise fully connected feed-forward network. for each sublayer. use LayerNorm(x+Sublayer(x)). all dimension=512.

Decoder:

1. The decoder is composed of a stack of N= 6 identical layers.
2. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.
3. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.

Main Take away from this model:

1. multi-head self attention: use self attention, linear transform multi-times to get projection of key-values, then do ordinary attention; 2) some tricks to improve performance(residual connection,position encoding, poistion feed forward, label smooth, mask to ignore things we want to ignore).

Use this model to do task classification:

Here we only use encode part for task classification, removed resdiual connection, used only 1 layer.no need to use mask. we use multi-head attention and postionwise feed forward to extract features of input sentence, then use linear layer to project it to get logits.

for detail of the model, please check: a2_transformer_classification.py

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11.Recurrent Entity Network

Input:1. story: it is multi-sentences, as context. 2.query: a sentence, which is a question, 3. ansewr: a single label.

Model Structure:

1. Input encoding: use bag of word to encode story(context) and query(question); take account of position by using position mask

   by using bi-directional rnn to encode story and query, performance boost from 0.392 to 0.398, increase 1.5%.

2. Dynamic memory:

a. compute gate by using 'similiarity' of keys,values with input of story.

b. get candidate hidden state by transform each key,value and input.

c. combine gate and candidate hidden state to update current hidden state.

3. Output moudle( use attention mechanism): a. to get possibility distribution by computing 'similarity' of query and hidden state

b. get weighted sum of hidden state using possibility distribution.

c. non-linearity transform of query and hidden state to get predict label.

Main take away from this model:

1. use blocks of keys and values, which is independent from each other. so it can be run in parallel.

2. modelling context and question together. use memory to track state of world; and use non-linearity transform of hidden state and question(query) to make a prediction.

3. simple model can also achieve very good performance. simple encode as use bag of word.

for detail of the model, please check: a3_entity_network.py

under this model, it has a test function, which ask this model to count numbers both for story(context) and query(question). but weights of story is smaller than query.

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12.Dynamic Memory Network

Outlook of Model:

1.Input Module: encode raw texts into vector representation

2.Question Module: encode question into vector representation

3.Episodic Memory Module: with inputs,it chooses which parts of inputs to focus on through the attention mechanism, taking into account of question and previous memory====>it poduce a 'memory' vecotr.

4.Answer Module:generate an answer from the final memory vector.

Detail:

1.Input Module:

a.single sentence: use gru to get hidden state b.list of sentences: use gru to get the hidden states for each sentence. e.g. [hidden states 1,hidden states 2, hidden states...,hidden state n]

2.Question Module: use gru to get hidden state

3.Episodic Memory Module:

use an attention mechanism and recurrent network to updates its memory.

a. gate as attention mechanism:

 two-layer feed forward nueral network.input is candidate fact c,previous memory m and question q. features get by take: element-wise,matmul and absolute distance of q with c, and q with m.

b.memory update mechanism: take candidate sentence, gate and previous hidden state, it use gated-gru to update hidden state. like: h=f(c,h_previous,g). the final hidden state is the input for answer module.

c.need for multiple episodes===>transitive inference.

e.g. ask where is the football? it will attend to sentence of "john put down the football"), then in second pass, it need to attend location of john.

4.Answer Module: take the final epsoidic memory, question, it update hidden state of answer module.

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TODO

1.Character-level Convolutional Networks for Text Classification

2.Convolutional Neural Networks for Text Categorization:Shallow Word-level vs. Deep Character-level

3.Very Deep Convolutional Networks for Text Classification

4.Adversarial Training Methods For Semi-supervised Text Classification

5.Ensemble Models

Conclusion:

During the process of doing large scale of multi-label classification, serveral lessons has been learned, and some list as below:

1. What is most important thing to reach a high accuracy? It depend the task you are doing. From the task we conducted here, we believe that ensemble models based on models trained from multiple features including word, character for title and description can help to reach very high accuarcy; However, in some cases,as just alphaGo Zero demonstrated, algorithm is more important then data or computational power, in fact alphaGo Zero did not use any humam data.

2. Is there a ceiling for any specific model or algorithm? The answer is yes. lots of different models were used here, we found many models have similiar performances, even though there are quite different in structure. In some extent, the difference of performance is not so big.

3. Is case study of error useful? I think it is quite useful especially when you have done many different things, but reached a limit. For example, by doing case study, you can find labels that models can make correct prediction, and where they make mistakes. And to imporove performance by increasing weights of these wrong predicted labels or finding potential errors from data.

4. How can we become expert in a specific of Machine Learning? In my opinion,join a machine learning competation or begin a task with lots of data, then read papers and implement some, is a good starting point. So we will have some really experience and ideas of handling specific task, and know the challenges of it. But what's more important is that we should not only follow ideas from papers, but to explore some new ideas we think may help to slove the problem. For example, by changing structures of classic models or even invent some new structures, we may able to tackle the problem in a much better way as it may more suitable for task we are doing.

Reference:

1.Bag of Tricks for Efficient Text Classification

2.Convolutional Neural Networks for Sentence Classification

3.A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

4.Deep Learning for Chatbots, Part 2 – Implementing a Retrieval-Based Model in Tensorflow, from www.wildml.com

5.Recurrent Convolutional Neural Network for Text Classification

6.Hierarchical Attention Networks for Document Classification

7.Neural Machine Translation by Jointly Learning to Align and Translate

8.Attention Is All You Need

9.Ask Me Anything:Dynamic Memory Networks for Natural Language Processing

10.Tracking the state of world with recurrent entity networks

11.Ensemble Selection from Libraries of Models

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to be continued. for any problem, concat brightmart@hotmail.com