C OMPACT E NCODING OF W ORDS FOR E FFICIENT C HARACTER - LEVEL C ONVOLUTIONAL N EURAL N ETWORK T EXT C LASSIFICATION
Anonymous authors
Paper under double-blind review
A BSTRACT
This paper reports the empirical exploration of a novel approach to encode words using compressing inspired techniques for use on convolutional neural networks at character-level.
This approach reduces drastically the number of parameters to be optimized using deep learning, resulting in competitive classiﬁcation accuracy values in only a fraction of the time spent by characters level convolutional neural networks, enabling training in simpler hardware.
I NTRODUCTION
Documents classiﬁcation is one of the key tasks addressed in the context of natural language processing (Sebastiani, 2002).
It implies associating a document —or a text fragment, for that matter— to a category or label relying on their content.
This is an important and daily need of modern life.
Everyday, we manipulate documents organizing them into folders, seeking to group characteristics, ideas and subjects for future references.
Such organization makes possible to relate other people’s thoughts and experiences for feed the development of our own ideas.
With the increasing availability of texts, especially through the Internet, the need for artiﬁcial intelligence tools for automate this function is increasing very fast.
Spam detectors, sentiment analysis, news archiving are all technologies based on text classiﬁers.
Work on document classiﬁcation encompasses a broad range of approaches (see (Sebastiani, 2002;
Aggarwal & Zhai, 2012; Hotho et al., 2005; Kosala & Blockeel, 2000)).
An important portion of those approaches rely on a representation that handles words as the atomic element of text.
Consequently, those models carry out their analysis through statistics of words occurrence (Zhang &
LeCun, 2015).
However, the variability of words and structures belonging to a language makes this task difﬁcult.
That is why, such models have superior quality mainly in speciﬁc domains, where the vocabulary can be restricted to a relatively small number of words, possibly chosen by a specialist.
Furthermore, such modeling becomes speciﬁc to a language, causing the replication process in another language to be carried out from scratch (Zhang & LeCun, 2015).
In recent years, we have experienced a revolution in the machine learning with the advent of deep learning methodologies (Goodfellow et al., 2016).
The development of convolutional neural networks (CNNs) (LeCun et al., 1998) coupled with the popularization of parallel computing libraries (e. g. Theano (Bergstra et al., 2010), Tensorﬂow (Abadi et al., 2015), Keras (Chollet et al., 2015), etc.)
that simplify general-purpose computing on graphics processing units (GPGPU) (Mittal &
Vetter, 2015) has been successful in tackling image classiﬁcation problem (Krizhevsky et al., 2012) quickly becoming the state of the art of the ﬁeld.
It was therefore natural that the deep learning principles were to be extended to the document classiﬁcation domain.
Some existing methods have been updated but the clear majority are still based on the tokenization of words and the inference of their statistics.
Bag of Words (BOW) (Johnson &
Zhang, 2014) and word2vec (Mikolov et al., 2013) are some of the most popular strategies.
The replication of image classiﬁcation success in the documents domain suffers, among other reasons, by the difﬁculty of representing one word as a vector.
Using the one hot encoding technique (Harris & Harris, 2012), where the vector of the term position is 1 and all other 0, would generate a representation of thousands or even millions of coordinates, making its use impractical both in terms of memory needed to store this vector, as time required to compute its optimization.
Another problem is that typing errors or simple declensions of words in test data could not be codiﬁed if they are unseen on train data.
In an groundbreaking paper, Zhang & LeCun (2015) suggested an approach that consider the character as the atomic level, reducing the vocabulary of symbols to only 69 characters and allowing the use of one hot encoding.
They considered each of the 1014 characters of a text represented by the line of a matrix where the column referring to the character of the vocabulary is 1 and all the others 0.
With this representation on hand, they applied a model with deep convolutional neural networks and obtained results competitive with other techniques, and in some cases the state of the art.
Building upon the work of Zhang & LeCun (2015), we suggest a novel approach to word representation that encode a word as a sequence of chars in a vector with size order of some hundreds, using the knowledge of character frequency imbalance, and inspired by traditional compression techniques.
This procedure made possible to represent larger portions of texts, perform the training in shorter times and applying simpler hardware, preserving the ability to codify any word, even unseen ones.
P RELIMINARIES
The success in using Convolutional Neural Networks (CNNs) (LeCun et al., 1998) in image classiﬁcation (Krizhevsky et al., 2012) ﬂourish the development of many libraries (Chetlur et al., 2014;
Chollet et al., 2015; Bergstra et al., 2010) , techniques and hardware.
The effort to use Neural Convolution Networks for text classiﬁcation tasks is justiﬁed by the possibility of appropriating these tools for obtaining better results and more robust algorithms, facilitating the use of the same approach for several applications.
There are two usual approaches to use CNN with text: Bag of Words (BOW)
(Johnson & Zhang, 2014) and word2vec (Mikolov et al., 2013).
In Bag of Words and some of its variants, for representing each word in a vocabulary of size n , a digit 1 is placed in the correspondent position of that word in a 1 × N vector, all others positions remaining with digit 0.
Since natural languages usually have a large vocabulary, a limited subset of the vocabulary must be used in order to make viable to perform the necessary computations in terms of memory requirements.
The chosen subset of the vocabulary must be representative of the texts.
Therefore, in practical problems a great deal of attention is devoted to this matter.
In particular, it is common to involve an application domain specialist or use some kind of word frequency statistic or relevance metric, where the most frequent and rare words are excluded.
In word2vec approach, each word is represented in a embedding of ﬁxed size, representing its cooccurrence in a large corpus of text in the same language of the texts of interest.
It is possible to use only pretrained vectors or readjust the representation with new words.
The main problem with both strategies is that it does not allows to represent words that are not in the training dataset.
Establishing the character as the basic unit of word formation provides a better opportunity to be robust to typos, acceptance of neologisms, equations and chemical formulas, abbreviations and idiosyncrasies of written language on the internet, such as emoticons, slang and dialects of the online world.
Assuming the word as a base item, much of this ability is lost, especially when models assumes that text producers uses a formal language.
The article by Zhang & LeCun (2015) was a great innovation in this regard.
The results obtained by them suggest that language can be treated as a sequence of signals, like any other (Zhang & LeCun, 2015).
However, the training times and the dimension of the matrices to be computed are still obstacles to the most effective use of the method.
Searching for a way to reduce these training time remaining with the ﬂexibility and power of character level convolutional to classify text, we found a way to better encode the text.
Our approach , with competitive accuracy, achieve a signiﬁcant reduction on time execution from hours to min and from days to hours, by epoch.
To achieve this performance in execution, our approach consisted of two elements:
• Obtaining a shorter representation : At ﬁrst we tough of using some form of encoding each character, and use the same approach of Zhang & LeCun
but we realize that using a variable length encoding for each word could be more efﬁcient.
To do this we need
a way to encode each char, generating distinct concatenated code for representing each word and that words with the same preﬁx was near each other, specially to respond well to declensions.
• Obtaining a sparse representation :
To take full advantage of libraries of tensor multiplications like NVidia CuDNN (Chetlur et al., 2014), we need a representation that was sparse, so our code should be composed of many 0 and a few 1.
Though Huffman encoding (Huffman, 1952) is the short possible, it does not generate unique representations once we concatenate char codes to form a word.
We investigate encodings of Brisaboa et al.
and found promising alternatives.
Our approach is based on Tagged Huffman (Silva de
Moura et al., 2000), where a pair of 0 digits is the signal and the digit 1 is the tag of begin and end code, the only difference is that for our approach, we need a shorter version to reduce the size of input matrix, so we choose to use only one digit 0 instead of two for each char, marking the beginning and the end of each char code with a digit 1 the same way.
As in the Silva de Moura et al.
(2000) approach, the coding we employ has the following advantages (Brisaboa et al., 2003):
No character code is a preﬁx of another, that is, the match is one-to-one. 2.
It allows direct search, that is, to search for a word in a codiﬁed document, just encode the word and use traditional methods of comparing strings with the encoded document.
This encoding approach is a compression technique, so it also allows saving already encoded text documents permanently using a binary system, requiring less storage space.
These advantages become attractive especially if the goal is to extract knowledge about ﬁles in a repository to perform various classiﬁcations on different dimensions with the same ﬁles.
A possible advantage of this encoding over others strategies that use a word as atomic representation of text is better respond to unseen words on training data, once that the network at least have some preﬁx to guess the meaning of the word, the same way we humans do.
This is especially interesting in languages where there are a lot of declensions like Portuguese, Spanish, Italian, French, Russian,
German, Arabic and Turkish, for example.
The coding procedure is not restricted to any size of vocabulary, the only problem is that less frequent characters will generate bigger codes, consequently, bigger encoding matrix .
If your database have a lot of them, you could use a higher word code size.
Our main contribution is demonstrate that such approach reduce the dimensionality of the encoding matrix, substantially reduce optimization time and allow the use of devices with lower computational power, remaining with competitive accuracy.
C OMPRESSED E NCODING FOR CNN- BASED T EXT C LASSIFICATION 3. E NCODING P ROCEDURE
In all of ours experiments, we used the following procedure to encode words:
• Obtaining a character frequency rank : We read the text database and count the frequency of each character, generating a list sorted by frequency of occurrence.
Then we create a rank with only the relative positions of the characters.
For a given language, this rank is quite stable since only the order of the rank is used.
This means that if all documents are in the same idiom, this procedure can be replaced by a list with the characters rank of frequency for that language.
• Creating a mapping from character to compress code : To encode each character, we insert the digit 0 in the same amount of the rank position of the character, between a 1 digit to signal the begin and another 1 digit to signal the end of code.
Table 1 have some examples of encoded characters.
[Table]
To encode each word, we just concatenate the codes of each character.
As an example, we provide in Table 2 some examples of plain text words and their corresponding encoding.
[Table]
TEXT
science scientist art artist tl;dr u  e a
Given a document, we consider that it is composed of words, being those any set of characters limited by the space character.
This means ‘word’ could be math equations, web addresses, L A TEX code, computer programming commands, etc.
In Table 2 we can see that this encoding could represent words with the same preﬁx in vectors that have same initial coordinates.
Slangs like tl;dr : too long, did not read and u2 : you too as well mathematical expressions like e a could be manipulated.
In the matrix representation of the document, each row represents a properly encoded word.
The code for each word is then embedded on a matrix of number of words × code size with its ﬁrst symbol in column 1.
Columns that were not occupied are padded with 0, larger codes are represented up to the chosen limit.
Unoccupied lines are ﬁlled with 0 and larger documents are represented only up to the chosen maximum number of words, ignoring the last remaining ones.
As an example, we represent a document in an 8x65 matrix in Figure 1:
We used 8x65 (520 elements) to encode the text with a certain amount of slack.
At the very least, we would need 7x64 (448 elements).
The approach of Zhang & LeCun (2015) would employ at least 69x32 (2208) elements to represent the same sentence.
In our experiments, 256 coordinates were enough to represent 99.5 of the words from one of the databases studied.
In all datasets studied in this paper, we choose 128 as a limit of words to represent a document, encoding each text in a 128x256 matrix.
E XPERIMENTAL S TUDY 4. D ATASETS
The databases used were the same as those cited in an article by Zhang et al.
(2015), where there is an extensive description of them.
We shared a link 1 where the reader could download it.
In this article, we will only summarize the main descriptions
• Ag news : categorized news articles from more than 2000 news sources.
Four classes (World, Sports, Business, SciTech).
Dataset contains 120k train samples and 7.6k test samples equally distributed (Zhang et al., 2015).
• Sogou news : categorized news articles originally in Chinese.
Zhang et al.
(2015) applied a pypinyin package combined with jieba Chinese segmentation system to produce Pinyin – a phonetic romanization of Chinese.
Five classes (sports, ﬁnance, entertainment, automobile and technology).
Dataset contains 450k train samples and 60k test samples equally distributed (Zhang et al., 2015).
• DBpedia : title and abstract from Wikipedia articles available in DBpedia crowd-sourced community (Lehmann et al., 2015).
Fourteen 14 nonoverlapping classes from DBpedia
Dataset contains 560k train samples and 70k test samples equally distributed (Zhang et al., 2015).
• Yelp full : sentiment analysis from the Yelp Dataset Challenge in 2015 2 .
Five classes representing the number of stars a user has given.
Dataset contains 560k train samples and 38k test samples equally distributed.
(Zhang et al., 2015).
• Yelp full : sentiment analysis from the Yelp Dataset Challenge in 2015 3 .
Rating of 1 andstars are represented as Bad and 4 and 5 as Good.
Dataset contains 560k train samples and 50k test samples equally distributed.
(Zhang et al., 2015).
• Yahoo answers : questions and their answers from Yahoo Answers.
Ten classes (Society & Culture, Science & Mathematics, Health, Education & Reference, Computers & Internet, Sports, Business & Finance, Entertainment & Music, Family & Relationships, Politics & Government).
Each sample contains question title, question content and best answer.
Dataset contains 1400k train samples and 60k test samples (Zhang et al., 2015).
Input 128x 126x 126x 126x 126x 42x 42x 42x 42x 168x 54x 50x 12x
Dense(#Class, So � max)
Dense 128, RELU
Fla � en 4 x Conv1d (256, 3 , Relu)
Conv1d (256, 5 , Relu) 164x
Conv1d (256, 5 , Relu)
Concatenate
MaxPoll1d (3)
Concatenate
LSTM
Dropout 0.
Dense 512, ELU 4 x Conv1d (256, 1 , Relu)
Input 128x 128x 128x 128x 128x 1x 1x 1x 1x 1x 1x
Dense(#Class, So � max)
MaxPoll1d
CNN CNN
MaxPoll1d (4)
MaxPoll1d (3)
• Amazon full : sentiment analysis from Amazon review dataset from the Stanford Network Analysis Project (SNAP) (McAuley & Leskovec, 2013).
Five classes representing the number of stars a user has given.
Dataset contains 3000k train samples and 650k test samples.(Zhang et al., 2015).
• Amazon polarity : sentiment analysis from Amazon review dataset from the Stanford Network Analysis Project (SNAP) (McAuley & Leskovec, 2013).
Two classes, rating of 1 and 2 stars are represented as Bad and 4 and 5 as Good.
Dataset contains 3000k train samples and 650k test samples.(Zhang et al., 2015).
Dataset contains 3600k train samples and 400k test samples equally distributed.(Zhang et al., 2015).
We do not use any preprocessing strategy except the use of lowercase letters.
No data enhancement technique was employed.
4. C OMPARISON M ODELS
The baseline comparison models are the same of Zhang et al.
(2015) where there is an extensive description of them, we just reproduce theirs results, the only difference is that they report loss error and for better comprehension, we translated it to accuracy.
In Zhang et al.
(2015) there is an extensive description of them.
In this paper, we just summarize the main information:
4.2. B ASELINE M ODELS
• Bag of Words (BOW) and its term-frequency inverse-document-frequency (Bow TFIDF) :
For each dataset, they selected 50,000 most frequent words from the training subset.
For the normal bag-of-words, they used the counts of each word as the features and for the TFIDF they used the counts as the term-frequency (Zhang et al., 2015).
• Bag-of-ngrams (Ngrams) and its TFIDF (Ngrams TFIDF) :
The bag-of-ngrams models were constructed by selecting the 500,000 most frequent n-grams (up to 5-grams) from the training subset for each dataset.
The feature values were computed the same way as in the bag-of-words model (Zhang et al., 2015).
• Bag-of-means on word embedding : Experimental model that uses k-means on word2vec (Mikolov et al., 2013) learnt from the training subset of each dataset, and then used these learnt means as representatives of the clustered words.
Took into consideration all the words that appeared more than 5 times in the training subset.
The dimension of the embedding is 300.
The bag-of-means features are computed the same way as in the bag-of-words model.
The number of means is 5000 (Zhang et al., 2015).
• Long Short Term Memory - LSTM :LSTM (Mikolov et al., 2013) model used in our case is word-based, using pretrained word2vec embedding of size 300.
The model is formed by taking mean of the outputs of all LSTM cells to form a feature vector, and then using multinomial logistic regression on this feature vector.
The output dimension is 512 (Zhang et al., 2015).
4.2. C HARACTER L EVEL C ONVOLUTIONAL N ETWORK M ODEL
The model that this paper builds upon is Zhang et al.
It encodes each char as an one-hot encoding in a vocabulary of 69.
The non-space characters are letters, numbers and punctuation.
The model is composed of 9 layers, 6 of convolutions and 3 fully connected.
Theirs architecture are described in Table 3.
They used stochastic gradient descent (SGD) with a minibatch of size 128, using momentum 0.9 and initial step size 0.01 which is halved every 3 epochs for 10 times.
So, their results were obtained in at least 30 epochs.
[Table]
Depends on the problem
4. N EURAL N ETWORK A RCHITECTURE
To verify the efﬁciency of the encoding procedure, we realized 2 experiments, named CNN1 and
CNN2:
CNN1 : At ﬁrst we choose a network architecture that we used to classify text using an embedding created by word2vec, the only difference is that instead of 300 features, we reduce the input size to 256.
This architecture we named CNN1.
It is based on concatenation of convolutions in a shallow way, inspired on work of Kim (2014), who achieve state of the art results for some databases.
We trained this model for 5 epochs.
The neural network architecture is described in Figure 2.
CNN2 : Enthusiasmed by the results of the ﬁrst experiment, we decided to investigate others possible architectures.
We created another shallow but wide convolution architecture following the recommendations of Zhang & Wallace (2015) for choosing parameters, executing training on dataset ag news, for being the shortest:
• Convolution width ﬁlter : a combination of region sizes near the optimal single best region size outperforms using multiple region sizes far from the optimal single region size (Zhang & Wallace, 2015).
We scan the width from 1 to 7 comparing accuracy performance.
In these evaluations, convolution of width 1 was a better option.
• Pooling size : max pooling consistently performs better than alternative strategies for the task of sentence classiﬁcation (Zhang & Wallace, 2015).
The architecture CNN2 is illustrated in Figure 2.
We trained this model for 12 epochs.
4. R ESULTS
For all the experiments, we used the environment and parameters settings listed on Table 4.
[Table] [Table]
All the results and comparison with traditional models and the approach of Zhang et al.
(2015) is show in Table 5.
On Table 6, we did a time execution comparison, based on reports available in
Zhang & LeCun (2015).
[Table]
D ISCUSSION
The main objective of this research was to evaluate the possibility of using a coding approach that contemplated the construction of words using characters as basic tokens.
Our main contribution is demonstrate that such approach allow reduce the dimensionality of the encoding matrix, allowing substantial shorter optimization times and the use of devices with lower computational power.
Some datasets of text have peculiarities that was not addressed by word frequency methods (BOW,
Word2vec).
The article of Zhang & LeCun (2015) was a great innovation in this regard.
However, the training times are still obstacles to the most effective use of the technique.
We though of a better representation should be a solution.
To make a comparison, perform the training of architecture CNN1 with an output of 4 classes make necessary to optimize 1,837,188 parameters.
As a comparison, in the architecture suggested by
Zhang and LeCun it is necessary to optimize 11,337,988 parameters (Zhang et al., 2015).
The major bottleneck for analyzing this gigantic amount of matrixed text is the need for intensive use of RAM.
Our approach generates a 128x256 matrix, smaller than 1014x69 generated by Zhang & LeCun (2015), but a large set of them quickly occupy the available memory on the computer.
On the machine we used, there were 16 GB available, which is not uncommon in modern personal computers.
Therefore, the use of generators to control the number of matrices generated and sent to
GPU is an important detail in the implementation of this optimization algorithm.
If your computer has only 8 GB of RAM or less, you should reduce the number of superbatch to ﬁt the memory.
The results that we obtained are very competitive with the approach o Zhang et al.
(2015) and traditional techniques.
Observing our results, we see even that we could ﬁnd parameters that results in excellent performance on ag news dataset, following the suggestions of Zhang & Wallace (2015).
One of advantage of a faster algorithm is that if your dataset is not so big, you could scan the feature width to ﬁnd a solution that optimize accuracy.
Another advantage is the possibility to realize kfolds validation, to have a better perspective on how well it will perform for your speciﬁc dataset on real life.
We are certain that our algorithm implementation could be even faster.
For our inability, we could not ﬁnd a way to make CPU and GPU working in parallel.
Using a GPU Geforce 1080ti, each of the superbatch of 10000 arrays have its weights updated in 30 seconds.
Only 6 seconds is consumed by GPU, another 24 seconds is spent in codifying all the matrix and delivery it on GPU.
Using a multithread strategy could help in this regard.
The results obtained strongly indicate that the use of this coding is a possibility.
We emphasize that we used is a fairly simple network, enough to demonstrate the feasibility of the encoding approach with the time and computational resources that we have.
Possibly there are several architectures that can generate better results.
In addition, the dimensionality reduction provided enables several architectures to be veriﬁed in a reasonable time.
F INAL R EMARKS
The main conclusion is that using a compressing technique is a very convenient possibility to represent words for use in Convolutional Neural Networks to classify text.
Codifying words using characters could be relevant specially on less curated texts datasets, being robust to typos, slangs and others usual characteristics of internet texts.
With our results, we reinforce Zhang et al.
(2015) conclusions, which states that language could be treated as a signal, no different of any other.
We demonstrated that using the approach suggested reduce the dimensionality of matrix representation of texts and allow the use of simpler hardware than character level one hot encoding, allowing a drastic reduction of training time.
We performed a comparative test with the approach of Zhang et al.
and others traditional techniques and found competitive results.
We speculate that new efforts should best employed in obtaining others architectures that may further favor this strategy.