{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:06:55Z","timestamp":1772813215619,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686547","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:p>Math Information Retrieval (MIR) aims to revolutionize the retrieval of math information by addressing the challenges in representing and retrieving highly specialized math content. To improve information retrieval accuracy in this field, we consider Document to Vector (Doc2Vec) and Formula to Vector (Formula2Vec) to determine content and formula similarity among math questions and their potential answers, respectively. This has great potential to advance the field of MIR by providing a new way to leverage the Doc2Vec and Formula2Vec models to enhance the accuracy of math query results. Doc2Vec is a powerful tool for comprehending and processing natural language. It leverages a neural network to acquire vector representations of textual content from math questions and answers, learning from their contextual usage in a given dataset. We have also examine the potential application of Formula2Vec in calculating the degree of resemblance between math formulas in questions and potential answers. Formula2Vec utilizes the logic of Word2Vec and applies it to formulas as if they were sentences. The proposed model, Two Vector Information Retrieval (2VecIR), applies both Doc2Vec and Formula2Vec in tandem, which enables a deep understanding of both the language within the text and the numeric or symbolic content of the formulas. In comparison to previous methods, our model has better precision, recall, and speed, contributing positively to the evolving landscape of MIR with advancements in the efficient, accurate, and context-based retrieval of relevant answers to math questions.<\/jats:p>","DOI":"10.3233\/faia260013","type":"book-chapter","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:43Z","timestamp":1772792443000},"source":"Crossref","is-referenced-by-count":0,"title":["Using Document and Formula Vectors to Retrieve Answers to Questions in Math"],"prefix":"10.3233","author":[{"given":"Angel","family":"Wheelwright","sequence":"first","affiliation":[{"name":"Computer Science Department, Brigham Young University, Provo, Utah 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiu-Kai","family":"Ng","sequence":"additional","affiliation":[{"name":"Computer Science Department, Brigham Young University, Provo, Utah 84602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA260013","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:44Z","timestamp":1772792444000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA260013"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"ISBN":["9781643686547"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia260013","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}