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

1) Candidate: algorithm


Is in goldstandard

1
paper corpusSignosTxtLongLines336 - : Algorithm: constructing data sets

2
paper corpusSignosTxtLongLines453 - : ^3Because RRG is a monostratal theory, syntax and semantics are directly linked without abstract syntactic representations or deep structures. In RRG, there is only one level of representation from the semantic representation of a clause or logical structure to the actual order of constituents. Thus, the theory poses a linking algorithm that contains a number of principles that “illustrate the workings of the syntax-semantics-pragmatics interface” (^[169]Van Valin, 2005: 128 ). One of the distinguishing properties of the RRG linking algorithm is the fact that it is bidirectional, that is, it connects the semantic and syntactic representations as well as the syntactic and semantic representations.

3
paper corpusSignosTxtLongLines455 - : First, the ABWSD method is compared against ^[123]Wang and Hirst (2014). They present a “Lesk-based algorithm which replaces the overlap mechanism of the Lesk algorithm with a general purpose Naive Bayes model” (^[124]Wang & Hirst, 2014: 1 ). The Naive Bayes model for word sense disambiguation (hereinafter known as NaiveBayesSM), computes the a posteriori probabilities of the senses of a polysemous word, then, the sense of the greater probability is chosen as the correct one. The experiments performed in ^[125]Wang and Hirst (2014) use the gloss description as the information source, a one-sentence context window, and stemming of the words in glosses and context. [126]Table 5 shows the F-score for the NaiveBayesSM and the ABWSD methods over the Senseval-2 corpus. For this comparison, the ABWSD methods were fitted with a random selection strategy for those cases when it is not able to provide an answer.

Evaluando al candidato algorithm:


1) semantic: 3 (*)
2) abwsd: 3
3) wang: 3
4) hirst: 3
5) syntactic: 3 (*)

algorithm
Lengua:
Frec: 79
Docs: 25
Nombre propio: 1 / 79 = 1%
Coocurrencias con glosario: 2
Frec. en corpus ref. en eng: 112
Puntaje: 2.683 = (2 + (1+4) / (1+6.32192809488736)));
Rechazado: muy común;

Referencias bibliográficas encontradas sobre cada término

(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)
algorithm
: Alfonseca, E. & Pérez, D. (2004). Automatic assessment of short questions with a Bleu-inspired algorithm and shallow NLP. Ponencia presentada en the 4th International Conference, EsTAL 2004, Alicante, España.
: Banerjee, S. & Pedersen, T. (2002). An adapted lesk algorithm for word sense disambiguation using wordnet. Proceedings of the CICLing 2002 Conference (pp. 136-145). LNCS: Springer-Verlag.
: Basile, P., Caputo, A. & Semeraro, G. (2014). An enhanced lesk word sense disambiguation algorithm through a distributional semantic model. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (pp. 1591-1600).
: Huang, Z., Chen, Y. & Shi, X. (2013). A novel word sense disambiguation algorithm based on semi-supervised statistical learning. International Journal of Applied Mathematics and Statistics™, 43(13), 452-458.
: Keerthi, S., Shevade, S., Bhattacharyya, C. & Murthy, K. (2001). Improvements to Platt’s SMO Algorithm for SVM Classifier Design. Neural Computation, 13(3), 637-649.
: Nazar, R. & Renau, I. (2016). A taxonomy of Spanish nouns, a statistical algorithm to generate it and its implementation in open source code. Ponencia presentada en el 10 th International Conference on Language Resources and Evaluation (LREC'16). European Language
: Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14, 130-137.
: Qiang, G. (2010). An effective algorithm for improving the performance on naive Bayes for text classification. En International conference on computer research and development. Kuala Lumpur, Malasia.
: Ramakrishnan, G., Prithviraj, B. & Bhattacharyya, P. (2004). A gloss-centered algorithm for disambiguation. Proceedings of SENSEVAL-3: Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text.
: Ramasubramanian, C. & Ramya, R. (2013). Effective pre-processing activities in text mining using improved porter’s stemming algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 2(12), 2278-1021.
: The statistical module of Atenea relies on the BiLingual Evaluation Understudy (BLEU) algorithm (Papineni, Roukos, Ward & Zhu, 2001). Basically, it looks for n-gram coincidences between the student's answer and the references. Its pseudocode is as follows:
: Van Valin, R. D. Jr & Mairal, R. (2014). Interfacing the Lexicon and an Ontology in a Linking Algorithm. In M. A. Gómez, F. Ruiz de Mendoza-Ibáñez & F. Gonzálvez-García (Eds.), Theory and Practice in Functional-Cognitive Space (pp. 205-228). Amsterdam: John Benjamins .
: Zhi-Hong, D., Tang, S.-W., Yang, D.-Q., Zhang, M., Wu, X. B. & Yang, M. (2002). Linear text classification algorithm based on category relevance factors. Ponencia presentada en el 5^th International Conference on Asian Digital Libra, Singapur.