{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T18:52:27Z","timestamp":1676659947592},"reference-count":54,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2013,3,12]],"date-time":"2013-03-12T00:00:00Z","timestamp":1363046400000},"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":[[2013,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper presents a classification-based approach to noun\u2013noun compound interpretation within the statistical learning framework of kernel methods. In this framework, the primary modelling task is to define measures of similarity between data items, formalised as kernel functions. We consider the different sources of information that are useful for understanding compounds and proceed to define kernels that compute similarity between compounds in terms of these sources. In particular, these kernels implement intuitive notions of lexical and relational similarity and can be computed using distributional information extracted from text corpora. We report performance on classification experiments with three semantic relation inventories at different levels of granularity, demonstrating in each case that combining lexical and relational information sources is beneficial and gives better performance than either source taken alone. The data used in our experiments are taken from general English text, but our methods are also applicable to other domains and potentially to other languages where noun\u2013noun compounding is frequent and productive.<\/jats:p>","DOI":"10.1017\/s1351324912000368","type":"journal-article","created":{"date-parts":[[2013,3,12]],"date-time":"2013-03-12T10:29:10Z","timestamp":1363084150000},"page":"331-356","source":"Crossref","is-referenced-by-count":3,"title":["Interpreting compound nouns with kernel methods"],"prefix":"10.1017","volume":"19","author":[{"given":"DIARMUID","family":"\u00d3 S\u00c9AGHDHA","sequence":"first","affiliation":[]},{"given":"ANN","family":"COPESTAKE","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2013,3,12]]},"reference":[{"key":"S1351324912000368_ref48","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809682"},{"key":"S1351324912000368_ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jml.2006.01.004"},{"key":"S1351324912000368_ref31","volume-title":"Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL-99)","author":"Lin","year":"1999"},{"key":"S1351324912000368_ref50","volume-title":"Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL-10)","author":"Tratz","year":"2010"},{"key":"S1351324912000368_ref51","doi-asserted-by":"publisher","DOI":"10.1162\/coli.2006.32.3.379"},{"key":"S1351324912000368_ref44","doi-asserted-by":"publisher","DOI":"10.1162\/coli.2007.33.2.161"},{"key":"S1351324912000368_ref47","volume-title":"Ordered Chaos: The Interpretation of English Noun-Noun Compounds","author":"Ryder","year":"1994"},{"key":"S1351324912000368_ref18","doi-asserted-by":"publisher","DOI":"10.1006\/brln.2001.2559"},{"key":"S1351324912000368_ref36","volume-title":"Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-08: HLT)","author":"Nakov","year":"2008"},{"key":"S1351324912000368_ref4","first-page":"695","volume-title":"Language Typology and Language Universals","author":"Bauer","year":"2001"},{"key":"S1351324912000368_ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-005-9007-5"},{"key":"S1351324912000368_ref39","unstructured":"\u00d3 S\u00e9aghdha D. 2008. 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