QEQR: An Exploration of Query Expansion Methods for Question Retrieval in CQA Services
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913583935258624 |
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| author | Ghafourian, Yasin Movahedi, Sajad Shakery, Azadeh |
| author_facet | Ghafourian, Yasin Movahedi, Sajad Shakery, Azadeh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15530 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QEQR: An Exploration of Query Expansion Methods for Question Retrieval in CQA Services Ghafourian, Yasin Movahedi, Sajad Shakery, Azadeh Information Retrieval Computation and Language Human-Computer Interaction 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. |
| title | QEQR: An Exploration of Query Expansion Methods for Question Retrieval in CQA Services |
| topic | Information Retrieval Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.15530 |