{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T18:22:32Z","timestamp":1768587752759,"version":"3.49.0"},"reference-count":14,"publisher":"MIT Press - Journals","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["TACL"],"published-print":{"date-parts":[[2016,12]]},"abstract":"<jats:p> Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as the primary objects to recover. To this end, we relate log co-occurrences of words in large corpora to semantic similarity assessments and show that co-occurrences are indeed consistent with an Euclidean semantic space hypothesis. Framing word embedding as metric recovery of a semantic space unifies existing word embedding algorithms, ties them to manifold learning, and demonstrates that existing algorithms are consistent metric recovery methods given co-occurrence counts from random walks. Furthermore, we propose a simple, principled, direct metric recovery algorithm that performs on par with the state-of-the-art word embedding and manifold learning methods. Finally, we complement recent focus on analogies by constructing two new inductive reasoning datasets\u2014series completion and classification\u2014and demonstrate that word embeddings can be used to solve them as well. <\/jats:p>","DOI":"10.1162\/tacl_a_00098","type":"journal-article","created":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T15:44:07Z","timestamp":1546011847000},"page":"273-286","source":"Crossref","is-referenced-by-count":36,"title":["Word Embeddings as Metric Recovery in Semantic Spaces"],"prefix":"10.1162","volume":"4","author":[{"given":"Tatsunori B.","family":"Hashimoto","sequence":"first","affiliation":[{"name":"CSAIL, Massachusetts Institute of Technology,"}]},{"given":"David","family":"Alvarez-Melis","sequence":"additional","affiliation":[{"name":"CSAIL, Massachusetts Institute of Technology,"}]},{"given":"Tommi S.","family":"Jaakkola","sequence":"additional","affiliation":[{"name":"CSAIL, Massachusetts Institute of Technology,"}]}],"member":"281","reference":[{"issue":"1","key":"p_2","first-page":"22","volume":"16","author":"Church Kenneth Ward","year":"1990","journal-title":"Computational Linguistics"},{"issue":"4","key":"p_3","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s11118-008-9101-9","volume":"29","author":"Croydon David A","year":"2008","journal-title":"Potential Analysis"},{"issue":"23","key":"p_6","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1080\/00437956.1954.11659520","volume":"10","author":"Harris Zellig S","year":"1954","journal-title":"Word"},{"key":"p_13","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00134"},{"issue":"1","key":"p_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1070\/RM1975v030n01ABEH001400","volume":"30","author":"Molchanov SA","year":"1975","journal-title":"Russian Mathematical Surveys"},{"issue":"3","key":"p_17","doi-asserted-by":"crossref","first-page":"402","DOI":"10.3758\/BF03195588","volume":"36","author":"Nelson Douglas L","year":"2004","journal-title":"Behavior Research Methods, Instruments, & Computers"},{"issue":"1","key":"p_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0010-0285(73)90023-6","volume":"5","author":"Rumelhart David E","year":"1973","journal-title":"Cognitive Psychology"},{"issue":"3","key":"p_22","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/0022-2496(80)90017-6","volume":"22","author":"Schwarz Gideon","year":"1980","journal-title":"Journal of Mathematical Psychology"},{"key":"p_23","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1111\/j.2517-6161.1979.tb01076.x","author":"Sibson Robin","year":"1979","journal-title":"Journal of the Royal Statistical Society. 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