{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:04:07Z","timestamp":1765357447604,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434297"},{"type":"electronic","value":"9783031434303"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43430-3_33","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T06:02:16Z","timestamp":1694844136000},"page":"372-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MWPRanker: An Expression Similarity Based Math Word Problem Retriever"],"prefix":"10.1007","author":[{"given":"Mayank","family":"Goel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V.","family":"Venktesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vikram","family":"Goyal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"33_CR1","doi-asserted-by":"publisher","unstructured":"Cobbe, K., et al.: Training verifiers to solve math word problems (2021). https:\/\/doi.org\/10.48550\/ARXIV.2110.14168. https:\/\/arxiv.org\/abs\/2110.14168","DOI":"10.48550\/ARXIV.2110.14168"},{"key":"33_CR2","doi-asserted-by":"publisher","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs (2017). https:\/\/doi.org\/10.48550\/ARXIV.1706.02216. https:\/\/arxiv.org\/abs\/1706.02216","DOI":"10.48550\/ARXIV.1706.02216"},{"key":"33_CR3","doi-asserted-by":"publisher","unstructured":"Huang, S., Wang, J., Xu, J., Cao, D., Yang, M.: Recall and learn: a memory-augmented solver for math word problems. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 786\u2013796. Association for Computational Linguistics, Punta Cana, Dominican Republic, November 2021. https:\/\/doi.org\/10.18653\/v1\/2021.findings-emnlp.68. https:\/\/aclanthology.org\/2021.findings-emnlp.68","DOI":"10.18653\/v1\/2021.findings-emnlp.68"},{"key":"33_CR4","doi-asserted-by":"publisher","unstructured":"Koncel-Kedziorski, R., Roy, S., Amini, A., Kushman, N., Hajishirzi, H.: MAWPS: a math word problem repository. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1152\u20131157. Association for Computational Linguistics, San Diego, California, June 2016. https:\/\/doi.org\/10.18653\/v1\/N16-1136. https:\/\/aclanthology.org\/N16-1136","DOI":"10.18653\/v1\/N16-1136"},{"key":"33_CR5","doi-asserted-by":"publisher","unstructured":"Kong, J., Swanson, H.: The effects of a paraphrasing intervention on word problem-solving accuracy of English learners at risk of mathematic disabilities. Learn. Disabil. Q. 42, 073194871880665 (2018). https:\/\/doi.org\/10.1177\/0731948718806659","DOI":"10.1177\/0731948718806659"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Lan, Y., et al.: MWPToolkit: an open-source framework for deep learning-based math word problem solvers (2021)","DOI":"10.1609\/aaai.v36i11.21723"},{"key":"33_CR7","doi-asserted-by":"publisher","unstructured":"Li, S., Wu, L., Feng, S., Xu, F., Xu, F., Zhong, S.: Graph-to-tree neural networks for learning structured input-output translation with applications to semantic parsing and math word problem (2020). https:\/\/doi.org\/10.48550\/ARXIV.2004.13781. https:\/\/arxiv.org\/abs\/2004.13781","DOI":"10.48550\/ARXIV.2004.13781"},{"key":"33_CR8","doi-asserted-by":"publisher","unstructured":"Li, Z., et al.: Seeking patterns, not just memorizing procedures: contrastive learning for solving math word problems (2021). https:\/\/doi.org\/10.48550\/ARXIV.2110.08464. https:\/\/arxiv.org\/abs\/2110.08464","DOI":"10.48550\/ARXIV.2110.08464"},{"key":"33_CR9","doi-asserted-by":"publisher","unstructured":"Liang, C.C., Wong, Y.S., Lin, Y.C., Su, K.Y.: A meaning-based statistical English math word problem solver. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 652\u2013662. Association for Computational Linguistics, New Orleans, Louisiana, June 2018. https:\/\/doi.org\/10.18653\/v1\/N18-1060. https:\/\/aclanthology.org\/N18-1060","DOI":"10.18653\/v1\/N18-1060"},{"key":"33_CR10","doi-asserted-by":"publisher","unstructured":"Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55\u201360. Association for Computational Linguistics, Baltimore, Maryland, June 2014. https:\/\/doi.org\/10.3115\/v1\/P14-5010. https:\/\/aclanthology.org\/P14-5010","DOI":"10.3115\/v1\/P14-5010"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Miao, S.Y., Liang, C.C., Su, K.Y.: A diverse corpus for evaluating and developing English math word problem solvers (2021)","DOI":"10.18653\/v1\/2020.acl-main.92"},{"key":"33_CR12","doi-asserted-by":"publisher","unstructured":"Nathan, M.J., Kintsch, W., Young, E.: A theory of algebra-word-problem comprehension and its implications for the design of learning environments. Cogn. Instr. 9(4), 329\u2013389 (1992). https:\/\/doi.org\/10.1207\/s1532690xci0904_2","DOI":"10.1207\/s1532690xci0904_2"},{"key":"33_CR13","doi-asserted-by":"publisher","unstructured":"Shridhar, K., Stolfo, A., Sachan, M.: Distilling multi-step reasoning capabilities of large language models into smaller models via semantic decompositions (2022). https:\/\/doi.org\/10.48550\/ARXIV.2212.00193. https:\/\/arxiv.org\/abs\/2212.00193","DOI":"10.48550\/ARXIV.2212.00193"},{"key":"33_CR14","doi-asserted-by":"publisher","unstructured":"Wu, Q., Zhang, Q., Wei, Z., Huang, X.: Math word problem solving with explicit numerical values. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 5859\u20135869. Association for Computational Linguistics, Online, August 2021. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.455. https:\/\/aclanthology.org\/2021.acl-long.455","DOI":"10.18653\/v1\/2021.acl-long.455"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43430-3_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T21:33:50Z","timestamp":1701034430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43430-3_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434297","9783031434303"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43430-3_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}