Task-Optimized Word Embeddings for Text Classification Representations
Sukrat Gupta, Teja Kanchinadam, Devin Conathan, Glenn Fung
Abstract
Word embeddings have introduced a compact and efficient way of representing text for further downstream natural language processing (NLP) tasks. Most word embedding algorithms are optimized at the word level. However, many NLP applications require text representations of groups of words, like sentences or paragraphs. In this paper, we propose a supervised algorithm that produces a task-optimized weighted average of word embeddings for a given task. Our proposed text embedding algorithm combines the compactness and expressiveness of the word-embedding representations with the word-level insights of a BoW-type model, where weights correspond to actual words. Numerical experiments across different domains show the competence of our algorithm.