QEQR: An Exploration of Query Expansion Methods for Question Retrieval in CQA Services

TL;DR

This paper investigates query expansion techniques to improve question retrieval in CQA services, addressing the lexical gap problem by proposing and evaluating various expansion methods, leading to notable performance gains.

Contribution

It introduces a novel question-similarity-based query expansion method and evaluates its effectiveness against existing approaches in CQA question retrieval.

Findings

Best method improves retrieval accuracy by 1.8% over baseline

Question-similarity-based expansion outperforms word-similarity methods

Selective expansion effectively mitigates the lexical gap

Abstract

CQA services are valuable sources of knowledge that can be used to find answers to users' information needs. In these services, question retrieval aims to help users with their information needs by finding similar questions to theirs. However, finding similar questions is obstructed by the lexical gap that exists between relevant questions. In this work, we target this problem by using query expansion methods. We use word-similarity-based methods, propose a question-similarity-based method and selective expansion of these methods to expand a question that's been submitted and mitigate the lexical gap problem. Our best method achieves a significant relative improvement of 1.8\% compared to the best-performing baseline without query expansion.