creators_name: Turney, Peter type: techreport datestamp: 2002-10-09 lastmod: 2011-03-11 08:55:03 metadata_visibility: show title: Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data. ispublished: unpub subjects: comp-sci-stat-model subjects: comp-sci-mach-learn subjects: comp-sci-art-intel full_text_status: public keywords: keyphrase extraction, unlabeled data, lexical knowledge, web mining, text mining abstract: A journal article is often accompanied by a list of keyphrases, composed of about five to fifteen important words and phrases that capture the article’s main topics. Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. Good performance on this task has been obtained by approaching it as a supervised learning problem. An input document is treated as a set of candidate phrases that must be classified as either keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase, the most important features (attributes) appear to be the frequency and location of the candidate phrase in the document. Recent work has demonstrated that it is also useful to know the frequency of the candidate phrase as a manually assigned keyphrase for other documents in the same domain as the given document (e.g., the domain of computer science). Unfortunately, this keyphrase-frequency feature is domain-specific (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of training documents in the given domain, with manually assigned keyphrases). The aim of the work described here is to remove these limitations. In this paper, I introduce new features that are conceptually related to keyphrase-frequency and I present experiments that show that the new features result in improved keyphrase extraction, although they are neither domain-specific nor training-intensive. The new features are generated by issuing queries to a Web search engine, based on the candidate phrases in the input document. The feature values are calculated from the number of hits for the queries (the number of matching Web pages). In essence, these new features are derived by mining lexical knowledge from a very large collection of unlabeled data, consisting of approximately 350 million Web pages without manually assigned keyphrases. date: 2002 date_type: published institution: National Research Council Canada department: Institute for Information Technology refereed: FALSE referencetext: Banko, M., Mittal, V., Kantrowitz, M., and Goldstein, J. (1999). Generating extraction-based summaries from hand-written summaries by aligning text spans. In Proceedings of the Pacific Rim Conference on Computational Linguistics (PACLING-99). Church, K.W., Hanks, P. (1989). Word association norms, mutual information and lexicogra-phy. Proceedings of the 27th Annual Conference of the Association of Computational Linguistics, pp. 76-83. Church, K.W., Gale, W., Hanks, P., Hindle, D. (1991). Using statistics in lexical analysis. In Uri Zernik (ed.), Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon, pp. 115-164. New Jersey: Lawrence Erlbaum. Domingos, P., and Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130. Dumais, S., Platt, J., Heckerman, D. and Sahami, M. (1998). Inductive learning algorithms and representations for text categorization. Proceedings of the Seventh International Conference on Information and Knowledge Management, pp. 148-155. ACM Press. Edmundson, H.P. (1969). New methods in automatic extracting. Journal of the Association for Computing Machinery, 16 (2), 264-285. Fayyad, U.M., and Irani, K.B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of 13th International Joint Confer-ence on Artificial Intelligence (IJCAI-93), pp. 1022-1027. Feelders, A., and Verkooijen, W. (1995). Which method learns the most from data? Method-ological issues in the analysis of comparative studies. Fifth International Workshop on Artificial Intelligence and Statistics, Ft. Lauderdale, Florida, pp. 219-225. Field, B.J. (1975). Towards automatic indexing: Automatic assignment of controlled-lan-guage indexing and classification from free indexing. Journal of Documentation, 31 (4), 246-265. Firth, J.R. (1957). A synopsis of linguistic theory 1930-1955. In Studies in Linguistic Analy-sis, pp. 1-32. Oxford: Philological Society. Reprinted in F.R. Palmer (ed.), Selected Papers of J.R. Firth 1952-1959, London: Longman (1968). Frank, E., Paynter, G.W., Witten, I.H., Gutwin, C., and Nevill-Manning, C.G. (1999). Domain-specific keyphrase extraction. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), pp. 668-673. California: Morgan Kauf-mann. Furnas, G., Landauer, T., Gomez, L., & Dumais, S. (1987). The vocabulary problem in human-system communication. Communications of the ACM, 30, 964-971. Gutwin, C., Paynter, G.W., Witten, I.H., Nevill-Manning, C.G., and Frank, E. (1999). Improving browsing in digital libraries with keyphrase indexes. Journal of Decision Sup-port Systems, 27, 81-104. Jones, S., and Paynter, G.W. (1999) Topic-based browsing within a digital library using key-phrases. Proceedings of Digital Libraries 99 (DL’99), pp. 114-121. ACM Press. Kupiec, J., Pedersen, J., and Chen, F. (1995). A trainable document summarizer. In E.A. Fox, P. Ingwersen, and R. Fidel, editors, SIGIR-95: Proceedings of the 18th Annual Interna-tional ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 68-73, New York: ACM. Landauer, T.K., and Dumais, S.T. (1997). A solution to Plato’ s problem: The Latent Seman-tic Analysis theory of the acquisition, induction, and representation of knowledge. Psy-chological Review, 104: 211-240. Leung, C.-H., and Kan, W.-K. (1997). A statistical learning approach to automatic indexing of controlled index terms. Journal of the American Society for Information Science, 48, 55-66. Lovins, J.B. (1968). Development of a stemming algorithm. Mechanical Translation and Computational Linguistics, 11, 22-31. Luhn, H.P. (1958). The automatic creation of literature abstracts. I.B.M. Journal of Research and Development, 2 (2), 159-165. Manning, C.D., and Schütze, H. (1999). Foundations of Statistical Natural Language Pro-cessing. Cambridge, Massachusetts: MIT Press. Martin, J., and Holte, R.C. (1998). Searching for content-based addresses on the World-Wide Web. Proceedings of The Third ACM Conference on Digital Libraries (DL’98). Soderland, S., and Lehnert, W. (1994). Wrap-Up: A trainable discourse module for informa-tion extraction. Journal of Artificial Intelligence Research, 2, 131-158. Sparck Jones, K. (1973). Does indexing exhaustivity matter? Journal of the American Soci-ety for Information Science, September-October, 313-316. Turney, P.D., and Halasz, M. (1993), Contextual normalization applied to aircraft gas turbine engine diagnosis, Journal of Applied Intelligence, 3, 109-129. Turney, P.D. (1997). Extraction of Keyphrases from Text: Evaluation of Four Algorithms. National Research Council, Institute for Information Technology, Technical Report ERB-1051. Turney, P.D. (1999). Learning to Extract Keyphrases from Text. National Research Council, Institute for Information Technology, Technical Report ERB-1057. Turney, P.D. (2000). Learning algorithms for keyphrase extraction. Information Retrieval, 2, 303-336. Turney, P.D. (2001). Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. Pro-ceedings of the Twelfth European Conference on Machine Learning (ECML-2001), Freiburg, Germany, pp. 491-502. Turney, P.D. (2002). Answering subcognitive Turing Test questions: A reply to French. Jour-nal of Experimental and Theoretical Artificial Intelligence. In press. van Rijsbergen, C.J. (1979). Information Retrieval. 2nd edition. London: Butterworths. Whitley, D. (1989). The GENITOR algorithm and selective pressure. Proceedings of the Third International Conference on Genetic Algorithms (ICGA-89), pp. 116-121. Califor-nia: Morgan Kaufmann. Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C. and Nevill-Manning, C.G. (1999) KEA: Practical automatic keyphrase extraction. Proceedings of Digital Libraries 99 (DL’99), pp. 254-256. ACM Press. Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., and Nevill-Manning, C.G. (2000). KEA: Practical Automatic Keyphrase Extraction. Working Paper 00/5, Department of Com-puter Science, The University of Waikato. citation: Turney, Peter (2002) Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data. [Departmental Technical Report] (Unpublished) document_url: http://cogprints.org/2497/1/ERB-1096.pdf