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For conducting the task of relation acquisition, a relaxed syntax-semantics method first extracts a group of explicit relation candidates. In parallel, a neural network miner acquires implicit relation candidates. The miner computes the vectors encoded by BERT to determine which implicit relations should be added. Thus, problem understanding can acquire both explicit relations and implicit relations, which addresses the challenge of building a problem understanding method that can acquire all the knowledge items to find the solution. In the subsequent step of symbolic solver, a fusion procedure forms a distilled set of relations from all the candidates by discarding unnecessary relations. Experimentation on nine benchmark datasets validates the superiority of the proposed algorithm that outperforms the state-of-the-art algorithms.<\/jats:p>","DOI":"10.1007\/s40747-022-00828-0","type":"journal-article","created":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T10:20:22Z","timestamp":1659090022000},"page":"697-717","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Solving arithmetic word problems by synergizing syntax-semantics extractor for explicit relations and neural network miner for implicit relations"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8379-6742","authenticated-orcid":false,"given":"Xinguo","family":"Yu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8299-1896","authenticated-orcid":false,"given":"Xiaopan","family":"Lyu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1208-3950","authenticated-orcid":false,"given":"Rao","family":"Peng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-7140","authenticated-orcid":false,"given":"Jun","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"issue":"9","key":"828_CR1","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1109\/TPAMI.2019.2914054","volume":"42","author":"D Zhang","year":"2019","unstructured":"Zhang D, Wang L, Zhang L, Dai BT, Shen HT (2019) The gap of semantic parsing: A survey on automatic math word problem solvers. 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