{"id":158437,"date":"2009-08-01T00:00:00","date_gmt":"2009-08-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/employing-topic-models-for-pattern-based-semantic-class-discovery\/"},"modified":"2018-10-16T20:20:31","modified_gmt":"2018-10-17T03:20:31","slug":"employing-topic-models-for-pattern-based-semantic-class-discovery","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/employing-topic-models-for-pattern-based-semantic-class-discovery\/","title":{"rendered":"Employing Topic Models for Pattern-based Semantic Class Discovery"},"content":{"rendered":"<div class=\"asset-content\">\n<p>A semantic class is a collection of items (words or phrases) which have semantically peer or sibling relationship. This paper studies the employment of topic models to automati-cally construct semantic classes, taking as the source data a collection of raw semantic classes (RASCs), which were extracted by ap-plying predefined patterns to web pages. The primary requirement (and challenge) here is dealing with multi-membership: An item may belong to multiple semantic classes; and we need to discover as many as possible the dif-ferent semantic classes the item belongs to. To adopt topic models, we treat RASCs as \u201cdoc-uments\u201d, items as \u201cwords\u201d, and the final se-mantic classes as \u201ctopics\u201d. Appropriate preprocessing and postprocessing are per-formed to improve results quality, to reduce computation cost, and to tackle the fixed-k constraint of a typical topic model. Experi-ments conducted on 40 million web pages show that our approach could yield better re-sults than alternative approaches.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A semantic class is a collection of items (words or phrases) which have semantically peer or sibling relationship. This paper studies the employment of topic models to automati-cally construct semantic classes, taking as the source data a collection of raw semantic classes (RASCs), which were extracted by ap-plying predefined patterns to web pages. The primary [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACL\/SIGPARSE","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL'09)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL'09)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Huibin Zhang, Mingjie 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