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

1) Candidate: clustering


Is in goldstandard

1
paper corpusSignosTxtLongLines546 - : Raoux, N., Amieva, H., Le Goff, M., Auriacombe, S., Carcaillon, L., Letenneur, L. & Dartigues, J. F. (2008). Clustering and switching processes in semantic verbal fluency in the course of Alzheimer’s disease subjects: results from the PAQUID longitudinal study . Cortex, 44(9), 1188-1196. [ [170]Links ]

Evaluando al candidato clustering:



clustering
Lengua:
Frec: 53
Docs: 14
Nombre propio: 1 / 53 = 1%
Coocurrencias con glosario:
Puntaje: 0.148 = ( + (1+0) / (1+5.75488750216347)));
Candidato aceptado

Referencias bibliográficas encontradas sobre cada término

(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)
clustering
: Bordag, S. (2006). Word sense induction: Triplet-based clustering and automatic evaluation. Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL) (pp. 137-144).
: Choudhary, B. & Bhattacharyya, P. (2003). Text clustering using universal networking language representation. Ponencia presentada en el 11th International World Wide Web Conference, Honolulu, Hawaii, Estados Unidos.
: Disambiguating company names in microblog text using clustering for online reputation management
: García, E. (2009). The motivated syntax of arbitrary signs. Cognitive constraints on Spanish clitic clustering. Amsterdam/Philadelphia: John Benjamins.
: Hotho, A., Maedche, A. & Staab, S. (2001). Ontology-based text clustering. Ponencia presentada en el International Joint Conference on Artificial Intelligence, Seattle, Estados Unidos.
: Ito, A., Lim, Y., Suzuki, M. & Makino, S. (2005). Pronunciation error detection method based on error rule clustering using a decision tree. In Proceedings of Interspeech, 173-176.
: Jin, P., Sun, X., Wu,Y. & Yu, S. (2007). Word clustering for collocation-based word sense disambiguation. In P. Jin, X. Sun,Y.Wu & S.Yu (Eds.), Lecture notes in computer science: Computational linguistics and intelligent text processing (pp. 267–274). Berlin: Springer-Verlag.
: Keikha, M., Razavian, N., Oroumchian, F. & Razi, H. S. (2008). (Eds). Document representation and quality of text: An analysis. En Survey of Text Mining II: Clustering, Classification, and Retrieval (pp. 135-168). Londres: Springer-Verlag.
: Key Words: Clustering of tweets, opinion analysis, disambiguation, online reputation management.
: Lee, S. & Jiang, J. (2014). Multilabel text categorization based on fuzzy relevance clustering. Fuzzy Systems IEEE Transactions, 22(6), 1457,1471.
: Pinto, D. (2008). On Clustering and Evaluation of Narrow Domain Short-Text Corpora. Unpublished doctoral dissertation, Universidad Politécnica de Valencia, Valencia, Spain.
: Pinto, D., Rosso, P. & Jimenez-Salazar, H. (2010). A self-enriching methodology for clustering narrow domain short texts. The Computer Journal. Oxford University Press.
: Pérez-Tellez, F., Cardiff, J., Rosso, P. & Pinto, D. (2015). Disambiguating company names in microblog text using clustering for online reputation management. Revista Signos. Estudios de Lingüística, 48(87), 54.
: Weakley, A. & Schmitter-Edgecombe, M. (2014). Analysis of verbal fluency ability in Alzheimer’s disease: The role of clustering, switching and semantic proximities. Archives of Clinical Neuropsychology, 29(3), 256-268.