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--- abstract: |- We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X:Y with some unspecified semantic relations, the corresponding output list of patterns is ranked according to how well each pattern Pi expresses the relations between X and Y. For example, given X=ostrich and Y=bird, the two highest ranking output patterns are "X is the largest Y" and "Y such as the X". The output patterns are intended to be useful for finding further pairs with the same relations, to support the construction of lexicons, ontologies, and semantic networks. The patterns are sorted by pertinence, where the pertinence of a pattern Pi for a word pair X:Y is the expected relational similarity between the given pair and typical pairs for Pi. The algorithm is empirically evaluated on two tasks, solving multiple-choice SAT word analogy questions and classifying semantic relations in noun-modifier pairs. On both tasks, the algorithm achieves state-of-the-art results, performing significantly better than several alternative pattern ranking algorithms, based on tf-idf. altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: 17-21 July 2006 conference: 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (ACL-06) confloc: 'Sydney, Australia' contact_email: ~ creators_id: - 2175 creators_name: - family: Turney given: Peter D. honourific: '' lineage: '' date: 2006 date_type: published datestamp: 2006-08-01 department: ~ dir: disk0/00/00/50/39 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 5039 fileinfo: /style/images/fileicons/application_pdf.png;/5039/1/NRC%2D48761.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'analogies, semantic relations, vector space model, noun-modifier expressions, latent relational analysis, pertinence' lastmod: 2011-03-11 08:56:33 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 313-320 pubdom: FALSE publication: ~ publisher: ~ refereed: TRUE referencetext: | Eugene Agichtein and Luis Gravano. 2000. 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Kernel methods for relation extrac-tion. Journal of Machine Learning Research, 3:1083-1106. relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 17:06:33 subjects: - comp-sci-lang - ling-comput - ling-sem - comp-sci-mach-learn - comp-sci-art-intel succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: Expressing Implicit Semantic Relations without Supervision type: confpaper userid: 2175 volume: ~