Improved Answer Selection with Pre-Trained Word Embeddings

2017/08/14 by Rishav Chakravarti, Jiří Navrátil, Chakravarti, Rishav +3 · 1 citation
Computer Science · #Expert finding and Q&A systems #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1708.04326

openalex publication_date 2017/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper evaluates existing and newly proposed answer selection methods based on pre-trained word embeddings. Word embeddings are highly effective in various natural language processing tasks and their integration into traditional information retrieval (IR) systems allows for the capture of semantic relatedness between questions and answers. Empirical results on three publicly available data sets show significant gains over traditional term frequency based approaches in both supervised and unsupervised settings. We show that combining these word embedding features with traditional learning-to-rank techniques can achieve similar performance to state-of-the-art neural networks trained for the answer selection task.

Citations

• Applying Deep Learning to Answer Selection: A Study and An Open Task

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• Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering

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