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

1) Candidate: classification


Is in goldstandard

1
paper corpusSignosTxtLongLines252 - : As can be observed from the findings, the way web designers interconnect hypertexts varies significantly from site to site. As regards the organizational point of view, Hammerich and Harrison (2002) from their study of business websites, proposed a classification of links that comprised two categories: strongly and weakly authored . Likewise, the same categories were found in our study. Out of the seventy two links analyzed, fifty were weakly authored and twenty two corresponded to the strongly-authored category. In the psychology hyperarticles, sixteen links were strongly authored and these belonged to the Kids Health website. The fact that all the strongly authored links were found in this website, might be because it is a site especially designed to provide families with accurate, up-to date doctor-approved health information about children and adolescents. That is, they have been designed with the aim of supplying specialized information to parents, teens and children. On the contrary,

2
paper corpusSignosTxtLongLines336 - : Before formulating the linguistic point we are going to test via computer experiments, we will first localize it within the vast realm of linguistics. Our statement is concerned with the concept of collocation, one of contemporary controversial issues in theoretical and applied linguistics. Knowledge of collocation is very important in lexicology (Herbst & Mittmann, 2008), translation (Boonyasaquan, 2006), language acquisition (Handl, 2008), and in various tasks of automated processing of natural language (e.g., in automatic word sense disambiguation: Jin, Sun, Wu & Yu, 2007; in machine translation: Wehrli, Seretan, Nerima & Russo, 2009; in text classification: Williams, 2002, etc .).

3
paper corpusSignosTxtLongLines374 - : Ronquillo, F., Pérez de Celis, C., Sierra, G., da Cunha, I. & Torres-Moreno, J. (2011). Automatic classification of biomedical texts: Experiments with a hearing loss corpus . En Y. Ding, Y. Peng, R. R. Shi, K. Hao & L. Wang (Eds.), 4th International Conference on Biomedical Engineering and Informatics (pp. 1674-1679). Shanghai, China: IEEE. [ [58]Links ]

4
paper corpusSignosTxtLongLines387 - : Abstract: Objective of present paper is to classify the comma uses focused in grammatical aspects and from a computational linguistic perspective is proposed. From this objective, some theoretical aspects based on grammatical criteria are showed, and the following classification of the comma functions are established: (i) indicator comma: it points enumerations and ellipsis, (ii) bounding comma: it delimits incidental clauses (appositions, vocatives, etcetera), and (iii) comma for disambiguation: it avoids confusion in expressions that could present more than a interpretation . Afterwards, a formalization and a computational implementation are made with the objective of getting a method of automatic detection for comma functions. In relation to the computational work, the software Smorph and Post Smorph Module (MPS) were used. Smorph analyzes the characters chain morphologically, giving an output with the morphological and categorical assignation for each occurrence according to the features

5
paper corpusSignosTxtLongLines416 - : We have analyzed in detail the errors typical to the method of Open IE based on heuristic rules over POS-tags. No detailed description or accurate classification of the errors had been reported before, although some types of errors along with some issues were mentioned by Fader et al. (2011), but not distinguished. We have distinguished between errors and their sources. We have classified all information extraction errors into four types based on the component of an extracted fragment where an error occurred: incorrect relation phrase, incorrect arguments, incorrect argument order, and incorrect arguments with correct relation phrase. This classification is complete: it covers all possible errors .

6
paper corpusSignosTxtLongLines541 - : Vandenberghe, R. (2016). Classification of the primary progressive aphasias: Principles and review of progress since 2011 . Alzheimer's Research & Therapy, 8(1), 16. [ [182]Links ]

7
paper corpusSignosTxtLongLines555 - : The allocation of natural language texts to one or more predefined categories or classes based on their content is an important component and a recent need in many information organization and management tasks. Automatic text classification is the task of categorizing documents to a predefined set of classes by a computational method or model. Text representation for classification purposes has been traditionally approached using a vector space model due to its simplicity and good performance. On the other hand, multi-label automatic text classification has been typically addressed either by transforming the problem under study to apply binary techniques or by adapting binary algorithms to work with multiple labels. In this article, the objective is to evaluate a term-weighting factor in the Boolean model for text representation in multi-label classification, using a mix of two approaches: problem transformation and model adaptation . This term-weighting factor and the combination of

8
paper corpusSignosTxtLongLines555 - : Alfaro, R. & Allende, H. (2011). Text representation in multi-label classification: Two new input representations . En A. Dobnikar, U. Lotrič & B. Šter (Eds.), Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science (pp- 61-70). Springer: Berlin, Heidelberg. [ [102]Links ]

9
paper corpusSignosTxtLongLines584 - : Menéndez, S. M. (2011b). Processes classification: Graduality, features and agentivity . Ponencia presentada en el 38th International Systemic Functional Congress - ISFC38 Negotiating difference: Languages, metalanguages, modalities, cultures. Facultad de Letras, Universidad de Lisboa, Lisboa, Portugal. [ [111]Links ]

Evaluando al candidato classification:


1) comma: 6 (*)
3) errors: 5 (*)
4) automatic: 5 (*)
7) computational: 4 (*)
8) incorrect: 4
9) objective: 4 (*)
11) representation: 3 (*)
14) smorph: 3
15) categories: 3
16) multi-label: 3
17) relation: 3
18) strongly: 3
20) authored: 3 (*)

classification
Lengua: eng
Frec: 148
Docs: 58
Nombre propio: 1 / 148 = 0%
Coocurrencias con glosario: 7
Puntaje: 7.808 = (7 + (1+5.64385618977472) / (1+7.21916852046216)));
Candidato aceptado

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(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)
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