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

TL;DR

This paper introduces Dense Cohort of Terms (dCoT), an unsupervised method that transforms sparse Bag of Words representations into dense features, improving text classification especially with limited or short texts.

Contribution

The paper presents dCoT, a novel unsupervised algorithm that reconstructs frequent words from co-occurring infrequent words, reducing sparsity and enhancing feature quality for text classification.

Findings

dCoT significantly improves classification accuracy on benchmark datasets.

The method effectively captures word co-occurrence patterns.

Dense features outperform traditional TF-IDF and Bag-of-Words representations.

Abstract

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