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We propose a different approach that is both computationally efficient and effective. Our solution combines a novel phrase mining framework to segment a document into single and multi-word phrases, and a new topic model that operates on the induced document partition. Our approach discovers high quality topical phrases with negligible extra cost to the bag-of-words topic model in a variety of datasets including research publication titles, abstracts, reviews, and news articles.<\/jats:p>","DOI":"10.14778\/2735508.2735519","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"305-316","source":"Crossref","is-referenced-by-count":129,"title":["Scalable topical phrase mining from text corpora"],"prefix":"10.14778","volume":"8","author":[{"given":"Ahmed","family":"El-Kishky","sequence":"first","affiliation":[{"name":"The University of Illinois, Urbana Champaign and Urbana, IL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanglei","family":"Song","sequence":"additional","affiliation":[{"name":"The University of Illinois, Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft Research and Redmond, WA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clare R.","family":"Voss","sequence":"additional","affiliation":[{"name":"Army Research Laboratory and Adelphi, MD"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Han","sequence":"additional","affiliation":[{"name":"The University of Illinois, Urbana Champaign and Urbana, IL"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,11]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"487","volume-title":"Proc. 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A phrase-discovering topic model using hierarchical pitman-yor processes. In Proc. EMNLP-CoNLL, pages 214--222, 2012."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/1699510.1699544"},{"key":"e_1_2_1_18_1","first-page":"2847","article-title":"Structural learning of chain graphs via decomposition","volume":"9","author":"Ma Z.","year":"2008","unstructured":"Z. Ma , X. Xie , and Z. Geng . Structural learning of chain graphs via decomposition . JMLR , 9 : 2847 , 2008 . Z. Ma, X. Xie, and Z. Geng. Structural learning of chain graphs via decomposition. JMLR, 9: 2847, 2008.","journal-title":"JMLR"},{"key":"e_1_2_1_19_1","volume-title":"Mallet: A machine learning for language toolkit","author":"McCallum A. K.","year":"2002","unstructured":"A. K. McCallum . Mallet: A machine learning for language toolkit . 2002 . A. K. McCallum. Mallet: A machine learning for language toolkit. 2002."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281246"},{"key":"e_1_2_1_21_1","first-page":"275","volume-title":"Proceedings of EMNLP","author":"Mihalcea R.","unstructured":"R. Mihalcea and P. Tarau . Textrank: Bringing order into texts . In Proceedings of EMNLP , page 275 . Barcelona, Spain, 2004. R. Mihalcea and P. Tarau. Textrank: Bringing order into texts. In Proceedings of EMNLP, page 275. Barcelona, Spain, 2004."},{"key":"e_1_2_1_22_1","volume-title":"M. I. T.","author":"Minka T.","year":"2000","unstructured":"T. Minka . Estimating a dirichlet distribution. Technical report , M. I. T. , 2000 . T. Minka. Estimating a dirichlet distribution. Technical report, M. I. T., 2000."},{"key":"e_1_2_1_23_1","volume-title":"Fishing for exactness. arXiv preprint cmp-lg\/9608010","author":"Pedersen T.","year":"1996","unstructured":"T. Pedersen . Fishing for exactness. arXiv preprint cmp-lg\/9608010 , 1996 . T. Pedersen. Fishing for exactness. arXiv preprint cmp-lg\/9608010, 1996."},{"key":"e_1_2_1_24_1","volume-title":"An algorithm for suffix stripping. Program: electronic library and information systems, 14(3): 130--137","author":"Porter M. F.","year":"1980","unstructured":"M. F. Porter . An algorithm for suffix stripping. Program: electronic library and information systems, 14(3): 130--137 , 1980 . M. F. Porter. An algorithm for suffix stripping. Program: electronic library and information systems, 14(3): 130--137, 1980."},{"key":"e_1_2_1_25_1","first-page":"100","volume-title":"Proc. EMNLP","author":"Schone P.","year":"2001","unstructured":"P. Schone and D. Jurafsky . Is knowledge-free induction of multiword unit dictionary headwords a solved problem . In Proc. EMNLP , pages 100 -- 108 , 2001 . P. Schone and D. Jurafsky. Is knowledge-free induction of multiword unit dictionary headwords a solved problem. In Proc. 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