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Lista de candidatos sometidos a examen:
1) mathematical (*)
(*) Términos presentes en el nuestro glosario de lingüística
1) Candidate: mathematical
Is in goldstandard
1
paper corpusSignosTxtLongLines336 - : The term function is used in the mathematical sense: f(X ) = Y. …Formally, a Lexical Function f is a function that associates with a given lexical expression L, which is the argument, or keyword, of f, a set {Li} of lexical expressions – the value of f – that express, contingent on L, a specific meaning associated with f:
2
paper corpusSignosTxtLongLines336 - : A computational technique used for tasks similar to the one we have just described, is called machine learning. In fact, machine learning is a class of methods developed in the area of artificial intelligence. These methods are based on various mathematical or statistical models and applied to extract knowledge from data: find data patterns, build structural description of data items, classify these items . The field of machine learning has its own concepts and terminology, and we are going to introduce some of the terms in the course of this section to make our exposition more exact. Pieces of data examined by machine learning techniques are called instances, or examples. In our case, an example is a set of all hyperonyms for a particular collocation as represented in [46]Figure 2. Examples can be annotated with respective lexical functions called classes in machine learning, or can be left without such annotation.
Evaluando al candidato mathematical:
2) lexical: 4 (*)
4) learning: 4
mathematical
Lengua: eng
Frec: 19
Docs: 13
Nombre propio: / 19 = 0%
Coocurrencias con glosario: 1
Puntaje: 1.784 = (1 + (1+3.16992500144231) / (1+4.32192809488736)));
Candidato aceptado
Referencias bibliográficas encontradas sobre cada término
(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)| mathematical |
: Guha, A. & Rossi, J. (2001). Convergence of the integration dynamics of the construction-integration model. Journal of Mathematical Psychology, 45, 355-369. : Hayes, R. (1963). Mathematical models in information retrieval. Natural language and the computers. New York: McGraw-Hill. : Jones, M. N., Willits, J. & Dennis, S. (2015). Models of semantic memory. Oxford handbook of Mathematical and Computational Psychology (pp. 232-254). Oxford: Oxford University Press. : MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability (pp. 281-297). Berkeley: University of California Press. : Martin, D. & Berry, M. (2007). Mathematical foundations behind latent semantic analysis. En T. Landauer, D. McNamara, S. Dennos & W. Kintsch (Eds.), Handbook of Latent Semantic Analysis (pp. 35-56). Mahwah, NJ.: Erlbaum. : Maslov, V. & Maslova, T. (2006). On Zipf’s Law and rank distributions in linguistics and semiotics. Mathematical Notes, 80(5), 679-691. : O’Halloran, K. (2005). Mathematical Discourse: Language, Symbolism and Visual Images. Londres/Nueva York: Continuum. : Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379-423. : Simon, H. (1957). A Behavioral Model of Rational Choice, in Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting. Nueva York: Wiley. |