{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:48:10Z","timestamp":1754156890763,"version":"3.41.2"},"reference-count":52,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2024,1,29]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-05-2022-0187","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T09:25:58Z","timestamp":1684401958000},"page":"1-23","source":"Crossref","is-referenced-by-count":0,"title":["A novel word-graph-based query rewriting method for question answering"],"prefix":"10.1108","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5107-4339","authenticated-orcid":false,"given":"Rongen","family":"Yan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7923-9329","authenticated-orcid":false,"given":"Depeng","family":"Dang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8987-3956","authenticated-orcid":false,"given":"Hu","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Wenhui","family":"Yu","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"issue":"No. 2","key":"key2024012913142803600_ref001","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1145\/990301.990303","article-title":"Learning to find answers to questions on the web","volume":"Vol. 4","year":"2004","journal-title":"ACM Transactions on Internet Technology (TOIT)"},{"issue":"No. 5","key":"key2024012913142803600_ref002","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1016\/j.ipm.2019.05.009","article-title":"Query expansion techniques for information retrieval: a survey","volume":"Vol. 56","year":"2019","journal-title":"Information Processing & Management"},{"issue":"No. 3","key":"key2024012913142803600_ref003","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1162\/089120105774321091","article-title":"Sentence fusion for multidocument news summarization","volume":"Vol. 31","year":"2005","journal-title":"Computational Linguistics"},{"key":"key2024012913142803600_ref004","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1145\/1367497.1367561","article-title":"Finding the right facts in the crowd: factoid question answering over social media","volume-title":"Proceedings of the 17th International Conference on World Wide Web","year":"2008"},{"key":"key2024012913142803600_ref005","first-page":"102","article-title":"An interface for annotating science questions","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations","year":"2018"},{"journal-title":"arXiv preprint arXiv:1806.00358","article-title":"A systematic classification of knowledge, reasoning, and context within the arc dataset","year":"2018","key":"key2024012913142803600_ref006"},{"key":"key2024012913142803600_ref007","first-page":"298","article-title":"Keyphrase extraction for n-best reranking in multi-sentence compression","year":"2013","journal-title":"Proceedings of NAACL-HLT 2013"},{"first-page":"426","article-title":"Efficient query evaluation using a two-level retrieval process","year":"2003","key":"key2024012913142803600_ref008"},{"key":"key2024012913142803600_ref009","article-title":"Ask the right questions: active question reformulation with reinforcement learning","volume":"abs\/1705.07830","year":"2017","journal-title":"arXiv preprint arXiv:1705.07830"},{"first-page":"69","volume-title":"Automatic Query Expansion Using Smart: Trec 3","year":"1995","key":"key2024012913142803600_ref010"},{"first-page":"353","article-title":"Building a question-answering corpus using social media and news articles","year":"2016","key":"key2024012913142803600_ref011"},{"key":"key2024012913142803600_ref012","article-title":"Reading Wikipedia to answer open-domain questions","volume":"abs\/1704.00051","year":"2017","journal-title":"arXiv preprint arXiv:1704.00051"},{"key":"key2024012913142803600_ref013","article-title":"Think you have solved question answering? 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