At Reputation.com, we work with millions of online reviews from hundreds of sources. One of the unusual characteristics of reviews compared to the vast majority of text corpora is that, almost by definition, reviews are structured in such a way that they can be categorized (in one or many dimensions depending on the review site and/or industry). However, we often find ourselves doing text classification/tagging on topics that are not already labeled by the review site. This article is an informal introduction to a set of techniques we have developed to leverage existing unlabeled corpora in conjunction with the labeled data. In particular, we present a semi-supervised learning algorithm for multi-label text classification.
In recent years, a lot of text classification projects have used supervised learning methods (Naive Bayes, SVM) primarily due to their substantial improvements over non-supervised strategies such as traditional clustering in NLP tasks. Until very recently, most NLP classification work was done with the traditional Bag of Words (BOW) approach – perhaps with a bit of context through the use of a limited range of N-grams and skip-grams. BOW is a feature extraction technique where the text is represented as the frequency of each word in the document, disregarding grammar and ordering but keeping multiplicity. In most cases, defining a pipeline combining the BOW feature extraction technique with a Tf-Idf transform and a simple classifier (Naive Bayes, SVM) produces decent results with respect to most classification metrics.
Semi-Supervised Learning with Word2Vec
In most tutorials, Word2Vec is presented as a stand-alone neural net preprocessor for feature extraction. Word2Vec generates a vector for each word in the text corpora in higher-dimensional space such that words that share contextual meaning are located in close proximity to one another. To use Word2Vec for classification, each word can be replaced by its corresponding word vector and usually combined through a naive algorithm such as addition with normalization or cross product to get a sentence or text vector. Then, using these document vectors we could use a simple classifier for multi-label classification. The advantage of using Word2Vec over a simple BOW feature extraction technique is it supports semi-supervised learning, since the vocabulary from the labeled and unlabeled text can be used to generate the word vectors. This allows the words to have more contextual meaning. However, we have found that this approach does not appear to provide significant improvements over a BOW approach especially when there isn’t a lot of labeled data for training the classifier.
Semantic Convolution for Low Support Topics
A common problem that is seen in multi-label text classification is a major imbalance of labels in a textual corpora. We often see cases where most (>60%) of the sampled data is about the most prevalent topic, and more than half the topic labels exist in <0.1% of the sampled data. Almost inherently with NLP and a BOW approach, this causes a p (number of features) >> n (size of training corpus) problem. Based on a general rules of thumb, getting 1,000 training examples for the low support topic would require millions of labeled training examples, which is prohibitively expensive.
In this world of ‘big data’ the data itself is actually cheap, but developing a tagged training set can be expensive. In the course of our development, we devised an elegant and scalable way to develop and maintain a robust training set across tens of industries (this will be the topic of a separate blog post).
The premise of Semantic Convolution is simple: if a particular word is a good indicator of a particular label, then words with similar meanings (semantics) should also be good indicators of the label. Since we have qualitative evidence that Word2Vec vectors encode a semantic meaning, we can use it to help find words with similar meanings from non-labeled corpora. This allows us to apply a Semantic transform after getting the term frequencies in the BOW pipeline, and before applying the Tf-Idf transform. To apply the Semantic Transform, we use the Word2Vec data to generate a correlation matrix between words with similar contextual meaning in the vocabulary.
vocabulary is a dictionary mapping each term with an index, the code to generate the
correlation_matrix = scipy.sparse.identity(len(vocabulary), format="dok") for idx, word in enumerate(vocabulary.keys()): similar_words =  try: similar_words = [x for x in word2vec_model.most_similar(word, topn=5) if x > 0.5] except: raise for similar_word in similar_words: if similar_word in vocabulary: correlation_matrix[vocabulary[word], vocabulary[similar_word]] = 1
Using this correlation matrix we can generate the term-document matrix with the augmented term frequencies.
term_frequency_vector += term_frequency_vector * correlation_matrix
Applying this transformation with the correlation matrix increases the word count of all words with contextually similar meaning in the text. This improves the feature collection for low support topics, which allows more precise classification of reviews about low support topics with higher confidence. This allows small amounts of labeled data to be more useful for the machine-learning model, which reduces the cost of developing a robust training set. Also, as mentioned above, this leverages semi-supervised learning from the unlabeled data by building the vocabulary and Word2Vec vectors based on the entire text corpora.
Ultimately the Semantic convolution provides more value from the little labeled data, and improves the performance of the machine leaning algorithm for classification tasks, especially the low support categories. Also, semi-supervised learning with Word2Vec leverages the information gained from the vast amounts of unlabeled data while increase both the precision and the support of the machine-learning model.
Dweep Shah and Anthony Johnson