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In text mining, information retrieval, and machine learning, text documentsare commonly represented through variants of sparse Bag of Words (sBoW) vectors(e.g. TF-IDF). Although simple and intuitive, sBoW style representations sufferfrom their inherent over-sparsity and fail to capture word-level synonymy andpolysemy. Especially when labeled data is limited (e.g. in documentclassification), or the text documents are short (e.g. emails or abstracts),many features are rarely observed within the training corpus. This leads tooverfitting and reduced generalization accuracy. In this paper we propose DenseCohort of Terms (dCoT), an unsupervised algorithm to learn improved sBoWdocument features. dCoT explicitly models absent words by removing andreconstructing random sub-sets of words in the unlabeled corpus. With thisapproach, dCoT learns to reconstruct frequent words from co-occurringinfrequent words and maps the high dimensional sparse sBoW vectors into alow-dimensional dense representation. We show that the feature removal can bemarginalized out and that the reconstruction can be solved for in closed-form.We demonstrate empirically, on several benchmark datasets, that dCoT featuressignificantly improve the classification accuracy across several documentclassification tasks.

An alternative text representation to TF-IDF and Bag-of-Words