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Update: February 24, 2023 The new version of Termout.org is now online, so this web site is now obsolete and will soon be dismantled. |
Lista de candidatos sometidos a examen:
1) information retrieval (*)
(*) Términos presentes en el nuestro glosario de lingüística
1) Candidate: information retrieval
Is in goldstandard
Evaluando al candidato information retrieval:
information retrieval
Lengua:
Frec: 42
Docs: 19
Nombre propio: / 42 = 0%
Coocurrencias con glosario:
Puntaje: 0.156 = ( + (1+0) / (1+5.4262647547021)));
Candidato aceptado
Referencias bibliográficas encontradas sobre cada término
(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)| information retrieval |
: Ault, T. & Yang, Y. (2002). Information filtering in TREC-9 and TDT-3: A comparative analysis. Journal of Information Retrieval, 5(2-3), 159-187. : Baeza-Yates, R. & Ribeiro-Neto, B. (1996). Modern information retrieval: The concepts and technology behind search. Reading, M.A.: Addison-Wesley. : Berry, M., Dumais, S. & O'Brien, G. (1994). Using linear algebra for intelligent information retrieval [en línea]. Disponible en: [30]http://lsirwww.epfl.ch/courses/dis/2003ws/papers/ut-cs-94-270.pdf : Fairthorne, R. (1961). The mathematics of the classification. Towards information retrieval. London: Butterwoths. : Harman, D. (1992). Relevance feedback and other query modification techniques. En W. Frakes & R. Baeza-Yates (Eds.), Information retrieval: Data structures and algorithms (pp. 241-236). Englewood Cliffs, NJ: Prentice. : Hayes, R. (1963). Mathematical models in information retrieval. Natural language and the computers. New York: McGraw-Hill. : Kolda, T. & O'Leary, D. (1998). A semi-discrete matriz descomposition for latent semantic indexing in information retrieval [en línea]. Disponible en: [54]http://portal.acm.org/citation.cfm?id=291131 : Larkey, L. (1998). Automatic essay grading using text categorization techniques. Proceedings of the 21^st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, U.S.A. : Lewis, D. & Ringuette, M. (1994). A comparison of two learning algorithms for text categorization.En Annual symposium on document analysis and information retrieval. Las Vegas, NV, USA. : Lewis, D.D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. Ponencia presentada en European Conference on Machine Learning, Chemnitz, Alemania. : Liu, J., Chang, W-Ch., Wu, Y. & Yang, Y. (2017). Deep learning for extreme multi-label text classification. En actas del 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17) (pp. 115-124). Nueva York, NY USA: ACM. : Manning, Ch., Raghavan, P. & Schütze, H. (2008). An introduction to information retrieval. Cambridge: Cambridge University Press. : Maron, M. & Kuhns, J. (1960). On relevance, probabilistic indexing, and information retrieval. Journal of the Association for Computing Machinery, 7(3), 216-244. : Nenkova, A. & McKeown, K. (2011). Automatic summarization. Foundations and Trends in Information Retrieval, 5(2-3), 103-233. : Paik, W., Liddy, E., Yu, E. & McKenna, M. (1993a). Interpretation of proper nouns for information retrieval. En M. Bates (Ed.), Proceedings of the ARPA workshop on human language technology, New Jersey, USA (pp. 1-5). San Francisco, CA: Morgan Kaufmann. : Rocchio, J. (1971). Relevance feedback in information retrieval. En G. Salton (Ed.), The SMART Retrieval SystemExperiments in automatic document processing (pp. 313-323). New Jersey: Prentice-Hall. : Salton, G. & McGill, M. (1983). Introduction to Modern Information Retrieval. New York, NY: McGraw-Hill. : Siddiqui, T. & Tiwary, U. S. (2008). Natural language processing and information retrieval. Nueva Dehli: Oxford University Press. : Tzoukermann, E., Klavans, J. & Strzalkowski, T. (2003). Information retrieval. En R. Mitkov (Ed.), The Oxford handbook of computational linguistics (pp. 530-544). New York: Oxford University Press. : Valente, A. (2005). Types and roles of legal ontologies. En V. R. Benjamins, P. Casanovas, J. Breuker & A. Gangemi (Eds.), Law and the Semantic Web: Legal ontologies, methodologies, legal information retrieval, and applications (pp. 65-76). Berlín-Heidelberg: Springer. : Van Rijsbergen, C. (1979). Information retrieval. Ontario: Butterworths. : Voorhees, E. & Harman, D. (2005). TREC: Experiments and evaluation in information retrieval. Nueva York: MIT Press. : Wilbur, W. & Kim, W. (2009). The ineffectiveness of within-document term frequency in text classification. Journal of Information Retrieval, 12(5), 509-525. : Wong, S., Ziarko, W. & Wong, P. (1985). Generalized vector space model in information retrieval. Proceedings of the 8^th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, U.S.A. : Zhang, T. & Oles, F. (2001). Text categorization based on regularized linear classification methods. Journal of Information Retrieval, 4(1), 5-31. : Zhong, Z. & Ng, H. T. (2012). Word sense disambiguation improves information retrieval. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers -Volume 1 (pp. 273-282). Association for Computational Linguistics. |