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The bag-of-words model is a standard representation of text for many linearclassifier learners. In many problem domains, linear classifiers are preferredover more complex models due to their efficiency, robustness andinterpretability, and the bag-of-words text representation can capturesufficient information for linear classifiers to make highly accuratepredictions. However in settings where there is a large vocabulary, largevariance in the frequency of terms in the training corpus, many classes andvery short text (e.g., single sentences or document titles) the bag-of-wordsrepresentation becomes extremely sparse, and this can reduce the accuracy ofclassifiers. A particular issue in such settings is that short texts tend tocontain infrequently occurring or rare terms which lack class-conditionalevidence. In this work we introduce a method for enriching the bag-of-wordsmodel by complementing such rare term information with related terms from bothgeneral and domain-specific Word Vector models. By reducing sparseness in thebag-of-words models, our enrichment approach achieves improved classificationover several baseline classifiers in a variety of text classification problems.Our approach is also efficient because it requires no change to the linearclassifier before or during training, since bag-of-words enrichment appliesonly to text being classified.

Word Vector Enrichment of Low Frequency Words in the Bag-of-Words Model for Short Text Multi-class Classification Problems