{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T17:14:34Z","timestamp":1648746874718},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"2","license":[{"start":{"date-parts":[[1999,6,1]],"date-time":"1999-06-01T00:00:00Z","timestamp":928195200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nat. Lang. Eng."],"published-print":{"date-parts":[[1999,6]]},"abstract":"<jats:p>This paper presents a system for automatic verb sense disambiguation using a small corpus \nand a Machine-Readable Dictionary (MRD) in Korean. The system learns a set of typical uses \nlisted in the MRD usage examples for each of the senses of a polysemous verb in the MRD \ndefinitions using verb-object co-occurrences acquired from the corpus. This paper concentrates \non the problem of data sparseness in two ways. First, by extending word similarity measures \nfrom direct co-occurrences to co-occurrences of co-occurring words, we compute the word \nsimilarities using non co-occurring words but co-occurring clusters. Secondly, we acquire IS-A \nrelations of nouns from the MRD definitions. It is possible to roughly cluster the nouns by \nthe identification of the IS-A relationship. Using these methods, two words may be considered \nsimilar even if they do not share any word elements. Experiments show that this method \ncan learn from a very small training corpus, achieving over an 86% correct disambiguation \nperformance without any restriction on a word's senses.<\/jats:p>","DOI":"10.1017\/s1351324999002193","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T13:30:22Z","timestamp":1027776622000},"page":"157-170","source":"Crossref","is-referenced-by-count":0,"title":["Verb sense disambiguation based on dual distributional similarity"],"prefix":"10.1017","volume":"5","author":[{"given":"JEONG-MI","family":"CHO","sequence":"first","affiliation":[]},{"given":"JUNGYUN","family":"SEO","sequence":"additional","affiliation":[]},{"given":"GIL CHANG","family":"KIM","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[1999,6,1]]},"container-title":["Natural Language Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1351324999002193","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T19:17:28Z","timestamp":1553887048000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1351324999002193\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1999,6]]},"references-count":0,"journal-issue":{"issue":"2","published-print":{"date-parts":[[1999,6]]}},"alternative-id":["S1351324999002193"],"URL":"https:\/\/doi.org\/10.1017\/s1351324999002193","relation":{},"ISSN":["1351-3249","1469-8110"],"issn-type":[{"value":"1351-3249","type":"print"},{"value":"1469-8110","type":"electronic"}],"subject":[],"published":{"date-parts":[[1999,6]]}}}