Document Processing with LinkIT David K. Evans, Judith L. Klavans and Nina Wacholder Columbia University Department of Computer Science and Center for Research on Information Acess 500 W. 120th Street New York, NY, 10027, USA {devans, klavans, nina}@cs.columbia.edu Abstract We present a linguistically-motivated technique for the recognition and grouping of simplex noun phrases (SNPs) called LinkIT. Our system has two key features: (1) we efficiently gather minimal NPs, i.e. SNPs, as precisely and linguistically defined and motivated in our paper ; (2) we apply a refined set of postprocessing rules to these SNPs to link them within a document. The identification of SNPs is performed using a finite state machine compiled from a regular expression grammar, and the process of ranking the candidate significant topics uses frequency information that is gathered in a single pass through the document. We evaluated the NP identification component of LinkIT and found that it outperformed other NP chunkers in precision and recall. The system is currently used in several applications which are described, such as web page characterization and multi-document summarization.,qwurgxfwlrq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inkIT may be freely licensed for research purposes. Information can be found at http://www.columbia.edu/cu/cria/linkit/ or contact the authors for more information.
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Jacquemin, Christian (1999). Syntagmatic and Paradigmatic Representations of Term Variation. In Proceedings of 37th Annual Meeting of the Association for Computational Linguistics (pp.341-348). University of Maryland, College Park, MD, USA. Klavans, Judith L., Nina Wacholder, (1998). Automatic Identification of Significant Topics in Domain-Independent Full Text Documents. In Proceedings of the Information and Data Management Workshop. Available at http://www.cs.pitt.edu/~panos/idm98/imported/nina.html Klavans, Judith L., Martin Chodorow, and Nina Wacholder, (1992). Building a Knowledge Base from Parsed Definitions. In Karen Jensen, Goerge Heidorn, Steve Richardson (Eds.) Natural Language Processing: The PLNLP Approach (Chapter 11) Kluwer. Marcus M. P., B. Santorini, and M. A. Marcinkiewicz, (1993). Building a Large Annotated Corpus of English: The Penn Treebank. In Computational Linguistics (19). McKeown, Kathleen R., Judith L. Klavans, Vasileios Hatzivassiloglou, Regina Barzilay and Eleazar Eskin, (1999). Towards Multidocument Summarization by Reformulation: Progress and Prospects. In Proceedings of the Sixteenth National Conference on Artificial Intelligence AAAI- 1999. Orlando, Florida. Negrilla, Stefan (1998). Clustering Algorithms Summer Project. Computer Science Report, Columbia University. Ramshaw, Lance A. and Mitchell P. Marcus (1995). Text Chunking Using Transformation-Based Learning. In Proceedings of the Third Association for Computational Linguistics Workshop on Very Large Corpora. Tolle, Kristin M. and Hsinchun Chen (2000). Comparing Noun Phrasing Techniques for Use with Medical Digital Library Tools. In Journal of the American Society for Information Science Association 51(4):352-370. Tolle, Kristin M. (1997). Improving Concept Extraction from Text Using Noun Phrasing Tools: An Experiment in Medical Information Retrieval. Master Thesis. University of Arizona, Department of Management Information Systems. Wacholder, Nina (1998). Simplex NPs clustered by head: a method for identifying significant topics in a document. In Proceedings of Workshop on the Computational Treatment of Nominals COLING-ACL, pp70-79. Montreal. Wacholder, Nina, Judith L. Klavans and David Kirk Evans (in progress). An Analysis of the Role of Grammatical Categories in a Statistical Information Retrieval System. Columbia University, Department of Computer Science.