{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T07:46:43Z","timestamp":1778312803193,"version":"3.51.4"},"reference-count":97,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2018,10,31]]},"abstract":"<jats:p>\n            Automatic Term Extraction (ATE) deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a refreshed perspective to this problem: instead of searching for such a \u2018one-size-fit-all\u2019 solution that may never exist, we propose to develop generic methods to \u2018enhance\u2019 existing ATE methods. We introduce SemRe-Rank, the first method based on this principle, to incorporate semantic relatedness\u2014an often overlooked venue\u2014into an existing ATE method to further improve its performance. SemRe-Rank incorporates word embeddings into a personalised PageRank process to compute \u2018semantic importance\u2019 scores for candidate terms from a graph of semantically related words (nodes), which are then used to revise the scores of candidate terms computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked\n            <jats:italic>K<\/jats:italic>\n            candidate terms (the average for a set of\n            <jats:italic>K<\/jats:italic>\n            \u2019s), or up to 28 percentage points in F1 measured at a\n            <jats:italic>K<\/jats:italic>\n            that equals to the expected real terms in the candidates (F1 in short). Compared to an alternative approach built on the well-known TextRank algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision at top\n            <jats:italic>K<\/jats:italic>\n            , or up to 17 points in F1.\n          <\/jats:p>","DOI":"10.1145\/3201408","type":"journal-article","created":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T12:14:36Z","timestamp":1530101676000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["SemRe-Rank"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8587-8618","authenticated-orcid":false,"given":"Ziqi","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Sheffield, Nottingham Trent University, Sheffield, UK"}]},{"given":"Jie","family":"Gao","sequence":"additional","affiliation":[{"name":"University of Sheffield, Sheffield, UK"}]},{"given":"Fabio","family":"Ciravegna","sequence":"additional","affiliation":[{"name":"University of Sheffield, Sheffield, UK"}]}],"member":"320","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2006.06.007"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL\u201909)","author":"Agirre E."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 8th Text REtrieval Conference.","author":"Ahmad Khurshid","year":"1999"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC\u201914)","author":"Aker Ahmet","year":"2014"},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. 402--411","author":"Aker Ahmet","year":"2013"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3115\/991250.991317"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2652524.2652530"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.15514\/ISPRAS-2014-26(4)-1"},{"key":"e_1_2_1_10_1","volume-title":"ATR4S: Toolkit with state-of-the-art automatic terms recognition methods in scala. 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