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Managing syntactic variation in text retrieval
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Proceedings of the 2005 ACM symposium on Document engineering table of contents
Bristol, United Kingdom
SESSION: Document searching, document annotation, and document metadata table of contents
Pages: 162 - 164  
Year of Publication: 2005
ISBN:1-59593-240-2
Authors
Jesús Vilares  Universidade da Coruña, Spain
Carlos Gómez-Rodríguez  Universidade da Coruña, Spain
Miguel A. Alonso  Universidade da Coruña, Spain
Sponsors
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Information Retrieval systems are limited by the linguistic variation of language. The use of Natural Language Processing techniques to manage this problem has been studied for a long time, but mainly focusing on English. In this paper we deal with European languages, taking Spanish as a case in point. Two different sources of syntactic information, queries and documents, are studied in order to increase the performance of Information Retrieval systems.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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A. Arampatzis, T. P. van der Weide, P. van Bommel, and C. Koster. Linguistically-motivated information retrieval. In Encyclopedia of Library and Information Science, volume 69, pages 201--222. Marcel Dekker, Inc, New York-Basel, 2000.
 
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J. Graña. Técnicas de Análisis Sintáctico Robusto para la Etiquetación del Lenguaje Natural. PhD thesis, University of A Coruña, Spain, 2000.
 
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M. Hearst, J. Pedersen, P. Pirolli, H. Schutze, G. Grefenstette, and D. Hull. Xerox site report: Four TREC-4 tracks. In The Fourth Text REtrieval Conference (TREC-4), pages 97--119, 1996.
 
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J. R. Hobbs, D. Appelt, J. Bear, D. Israel, M. Kameyama, M. Stickel, and M. Tyson. FASTUS: A cascaded finite-state transducer for extracting information from natural-language text. In Finite-State Language Processing. MIT Press, 1997.
 
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C. Jacquemin and E. Tzoukermann. NLP for term variant extraction: synergy between morphology, lexicon and syntax. In Strzalkowski {10}, pages 25--74.
 
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J. Rocchio. The SMART Retrieval System - Experiments in Automatic Document Processing, chapter Relevance feedback in information retrieval, pages 313--323. Prentice-Hall, NJ, 1971.
 
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J. Vilares and M. A. Alonso. A grammatical approach to the extraction of index terms. In International Conference on Recent Advances in Natural Language Processing, Proceedings, pages 500--504, Borovets, Bulgaria, 2003.
 
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J. Vilares, M. A. Alonso, F. J. Ribadas, and M. Vilares. COLE experiments at CLEF 2002 Spanish monolingual track. In volume 2785 of Lecture Notes in Computer Science, pages 265--278. Springer-Verlag, Berlin-Heidelberg-New York, 2003.
 
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Collaborative Colleagues:
Jesús Vilares: colleagues
Carlos Gómez-Rodríguez: colleagues
Miguel A. Alonso: colleagues