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Lista de candidatos sometidos a examen:
1) natural language processing (*)
(*) Términos presentes en el nuestro glosario de lingüística

1) Candidate: natural language processing


Is in goldstandard

1
paper corpusSignosTxtLongLines336 - : What evidence have we obtained concerning lexical functions? We presented a sufficient number of collocations annotated with lexical functions to the computer that learned characteristic features of each function. It was demonstrated that the computer was able to assign lexical functions to unseen collocations with a significant average accuracy of 0.759. Is it satisfactory? We can compare our result with computer performance on another task of natural language processing: word sense disambiguation, i .e., identifying the intended meanings of words in context. Today, automated disambiguating systems reach the accuracy of about 0.700 and this is considered a substantial achievement. As an example of such works see (Zhong & Tou Ng, 2010). Therefore, our result is weighty enough to be a trustworthy evidence for the linguistic statement under discussion.

Evaluando al candidato natural language processing:


1) lexical: 3 (*)

natural language processing
Lengua: eng
Frec: 83
Docs: 37
Nombre propio: / 83 = 0%
Coocurrencias con glosario: 1
Puntaje: 1.406 = (1 + (1+2) / (1+6.39231742277876)));
Candidato aceptado

Referencias bibliográficas encontradas sobre cada término

(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)
natural language processing
: Baker, C. (2014). FrameNet: A knowledge base for natural language processing. In Proceedings of Frame Semantics in NLP: A workshop in honor of Chuck Fillmore (1929-2014) (pp. 1-5). Baltimore, Maryland.
: Berger, A., Della Pietra S. & Della Pietra, S. (1996). A maximum entropy approach to natural language processing, Computational Linguistics, 22(1), 39-71.
: Brill, E. (1995). Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistics, 21, 543-566.
: Carpuat, M. & Wu, D. (2007). Improving statistical machine translation using word sense disambiguation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learnin (pp. 61-72).
: Gaidhane, M. S. C., Gondhale, M. D. P. & Talole, M. P. (2015). A comparative study of stemming algorithms for natural language processing. International Journal of Engineering, Education and Technology (ARDIJEET), 3(2), 1-6.
: Gliozzo, A., Magnini, B. & Strapparava, C. (2004). Unsupervised domain relevance estimation for word sense disambiguation. Proceedings of the Empirical Methods in Natural Language Processing Conference, Barcelona, Spain.
: Hanks, P. & Pustejovsky, J. (2005). A pattern Dictionary for Natural Language Processing. Revue Française de linguistique appliquée, 10(2), 63-82.
: Hanks, P. (2009). The linguistic double helix: norms and exploitations. En D. Hlavácková, A. Horák , K. Osolsobě & P. Rychlý (Eds.), After half a century of Slavonic natural language processing (Festschrift for Karel Pala) (pp. 63-80). Brno: Masaryk University .
: Ide, N., Erjavec, T. & Tufis, D. (2001). Automatic sense tagging using parallel corpora. In Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium -NLPRS (pp. 83-90).
: Jackson, P. & Moulinier, I. (2003). Natural language processing for online applications. Text retrieval, extraction and categorization. Philadelphia: Benjamins.
: Jurafsky, D. & Martin, J. (2000). An introduction to natural language processing, computational linguistics and speech processing, Prentice Hall.
: Luzondo, A. & Ruiz de Mendoza, F. (2015). Argument structure constructions in a Natural Language Processing environment. Language Sciences, 48(2015), 70-89.
: Manning, C. & Schütze, H. (1999). Foundations of statistical natural language Processing. Boston: The MIT Press.
: Mausam, Schmitz, M., Bart, R., Soderland, S. & Etzioni, O. (2012). Open language learning for Information Extraction. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL ‘12), 523-534.
: Maynard, D. & Ananiadou, S. (2000). TRUCKS: A model for automatic multi-word term recognition. Journal of Natural Language Processing, 8(1), 101-125.
: Mel’čuk, I. (1996). Lexical functions: A tool for the description of lexical relations in a lexicon. In L. Wanner (Ed.), Lexical functions in lexicography and natural language processing (pp. 37–102). Amsterdam, Philadelphia: Johm Benjamins.
: Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing order into texts. Ponencia presentada en Conference on Empirical Methods in Natural Language Processing,Barcelona, España.
: Miikkulainen, M., & Dyer, M. (1991). Natural language processing with modular PDP networks and distributed lexicon. Cognitive Science, 15, 345-399.
: Murawaki, Y. (2013). Global model for hierarchical multi-label text classification. International Joint Conference on Natural Language Processing, 46-54.
: Pennacchiotti, M. & Pantel, P. (2009). Entity extraction via ensemble semantics. En Proceedings of Conference on Empirical Methods in Natural Language Processing. Singapore: ACL.
: Pérez, D., Alfonseca, E., Rodriguez, P., Gliozzo, A., Strapparava, C, &Magnini, B. (2005). About the effects of combining Latent Semantic Analysis with natural language processing techniques for free-text assessment. Revista Signos, 38(59), 325-343.
: Raskin, V. (1987). Linguistics and natural language processing. In S. Nirenburg (Ed.), Machine translation: Theoretical and methodological issues (pp. 42-58). Cambridge: Cambridge University Press.
: Rosé, C., Roque, A., Bhembe, D. & VanLehn, K. (2003). A hybrid text classification approach for analysis of student essays. Proceedings of the HLT-NAACL workshop Building Educational Applications Using Natural Language Processing, Edmonton, Canada.
: Siddiqui, T. & Tiwary, U. S. (2008). Natural language processing and information retrieval. Nueva Dehli: Oxford University Press.
: Teufel, S., Siddharthan, A. & Tidhar, D. (2006). Automatic classification of citation function. Ponencia presentada en the Conference on Empirical Methods in Natural Language Processing, Sydney, Australia.
: The internal architecture of Atenea is composed of a statistical module, called ERB, and several Natural Language Processing (NLP) modules based on the wraetlic tools (Alfonseca, 2003).
: Tolone, E. (2012). Conversión de las tablas del Léxico-Gramática del francés en el léxico LGLex. Ponencia presentada en el 2nd Argentinian Workshop on Natural Language Processing (WNLP’11), Córdoba, Argentina.
: Vickrey, D., Biewald, L., Teyssier, M. & Koller, D. (2005). Word-sense disambiguation for machine translation. Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (pp. 771-778). Association for Computational Linguistics.
: Zhou F., Zhang, F. & Yang, B. (2010). Graph-based text representation model and its realization. Ponencia presentada en el International Conference on Natural Language Processing and Knowledge Engineering, Beijing, China.