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

1) Candidate: computer


Is in goldstandard

1
paper corpusSignosTxtLongLines179 - : Graesser, A., Moreno, K., Marineau, J., Adcock, A., Olney, A. & Person, N. (2003). AutoTutor improves deep learning of computer literacy: Is it the dialog or the talking head ? En U. Hoppe, F. Verdejo & J. Kay (Eds.), Proceedings of Artificial Intelligence in Education (pp. 47-54). Amsterdam: IOS Press. [ [53]Links ]

2
paper corpusSignosTxtLongLines313 - : Verbs are often created from English verbs or nouns using Romanian verbal classifiers, like derivative suffixes: -a: forcasta (cf. forecast), targheta^[32]8 (cf. target), printa (cf. print); -iza: sponsoriza (cf. sponsor), globaliza (cf. global / globalize), computeriza (cf. computer / computerise), -ui^[33]9: a brandui (cf . brand), a bipui (cf. the interjection bip), a chatui (cf. chat), a serui (cf. share), a zipui (cf. zip) or inflectional suffixes: downloadati fisierul [download the file]: femeia care îl body-guard-eaza pe N. [the woman that *body-guards N.] (GALR, 2005). The affix-(a)re is specialized for abstract nouns, and is used as a means of completing the lexical family of the loanword: auditare, forcastare, printare, targhetare, etc.

3
paper corpusSignosTxtLongLines313 - : Examples of semantic calques in professional lexis may be found in the terminology of computer science (and some in business terminology), but it is important to point out that they are not very specialized terms and some of them are used in parallel to their foreign counterpart: a aplica (Engl . to apply), a descarca un fisier (Engl. to download a file), a licentia^[34]10 (Engl. to licence), a naviga (Engl. to surf), a opera (Engl. to operate), provocare (Engl. challenge), portofoliu de produse (Engl. product portfolio), promotie (Engl. promotion), virus (Engl. virus), vierme, (Engl. worm)^[35]11, etc.

4
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.

5
paper corpusSignosTxtLongLines337 - : Judgment of casual events was assessed by creating three different forms of computer animation based upon the Michottean launching paradigm: 1 ) direct causation (DC), 2) indirect causation (IC), and 3) non-causal (NC). Each animation consisted of 2 balls, an orange ball to the left of the screen, and a purple ball in the middle of the screen. In the IC and NC conditions, a blue cylinder lay equidistant between the horizontal path created by the orange and purple balls. At the beginning of each animation sequence, the orange ball began to move to the right. In the DC condition, the orange ball would ‘strike’ the purple ball, at which point the orange ball would stop moving and the purple ball would begin moving to the right. In the IC condition, the orange ball would ‘strike’ the blue cylinder and stop moving. The blue cylinder would then begin moving towards the stationary purple ball. The cylinder would then ‘strike’ the purple ball and come to rest, at which point the purple ball would

6
paper corpusSignosTxtLongLines340 - : En el ámbito de la interdisciplina lingüística Computer Assisted Language Learning (CALL), la importancia de los analizadores automáticos fue muy discutida en la última década por varios investigadores: Nagata, Matthews, Holland, Maisano, entre otros (en Heift & Schulze, 2007 ; Schulze, 2008). Holland, Kaplan y Sams (1995) se refieren a las posibilidades y limitaciones de tutores de lengua basados en analizadores automáticos. Para ello, realizan una comparación entre sistemas CALL convencional y sistemas ICALL (Intelligent Computer Assisted Language Learning) basado en analizadores automáticos y concluyen que en sistemas ICALL el estudiante puede escribir una gran variedad de oraciones y desarrollar de una forma relativamente libre habilidades para el mejoramiento de la producción escrita. La eficacia de los analizadores automáticos en la enseñanza de lenguas fue analizada empíricamente por Jouzulynas (1994, en Heift & Schulze, 2007) quien evaluó su utilidad en el diagnóstico de errores.

7
paper corpusSignosTxtLongLines389 - : Los nodos que se generan a partir de la relación nsubj son Bell, AGNT (agente) y distribute (distribuir), y para la relación dobj son THME (tema) y Computer:{*} (número indeterminado de computadoras ). Las características sintácticas del concepto, por ejemplo, en el caso del verbo, se mantienen codificadas en el nodo correspondiente tal como distributes (etiqueta VBZ generada por el parser que significa: verbo, tercera persona del singular, tiempo presente) y solo la palabra normalizada (verbo en infinitivo) se muestra en el grafo. El conjunto de etiquetas para las categorías gramaticales usadas por el parser de Stanford es el definido en el marco del proyecto Penn TreeBank (Santorini, 1990).

8
paper corpusSignosTxtLongLines400 - : “Indeed, it is probably time for linguists to reconsider our traditional assumptions about what makes a model ‘elegant’ […] [I]t may be that we should not condemn as ‘inelegant’ or ‘uneconomical’ rules that the conscious human mind finds somewhat difficult to implement, but which can be performed by a computer in a moment –and also, some might wish to add, whose analogues in human brain can similarly be performed in a trice and, moreover, without requiring conscious attention” (Fawcett, 2003: 13 ).

9
paper corpusSignosTxtLongLines459 - : El Análisis de Errores, basado en los procedimientos del Corpus de aprendices (CLC, del inglés Computer Learner Corpora) y en el Análisis de Errores asistido por Computador (CEA, del inglés Computer aided Error Analysis) en lo que se refiere a Corpora de Aprendientes de ELE en Formato Electrónico, ha evidenciado que los errores de mayor frecuencia y recurrencia corresponden a los errores ortográficos (^[31]Ferreira, 2014a ; ^[32]Ferreira, Elejalde & Vine, 2014). Los estudios se han sustentado en el corpus CAELE (^[33]Ferreira, 2015) constituido por una colección de 418 textos de aprendientes de ELE, almacenados y procesados en formato digital. Estos textos han sido recolectados durante intervenciones lingüísticas entre los años 2014 y 2015 con el objeto de describir la interlengua de los aprendices e identificar los errores lingüísticos más frecuentes y recurrentes según el nivel de competencia (proficiency, en inglés, es decir la capacidad que una persona demuestra en el uso de una lengua

10
paper corpusSignosTxtLongLines547 - : A mediados de la década de los setenta inicia la inclusión de tecnología computacional para el almacenamiento de datos de investigación lingüística y la producción de atlas lingüísticos (^[41]Hoch & Hayes, 2010). La primera publicación realizada sobre el uso de BDE y modelamiento computacional fue realizada por Alan Richard Thomas en 1980 en el trabajo Areal Analysis of Dialect Data by Computer: A Welsh Example . En este texto, Thomas presentó un temprano ejemplo de las posibilidades de uso de un SIG para medir la correlación espacial en datos computarizados de su investigación lingüística. Thomas logró el almacenamiento y visualización de múltiples atributos lingüísticos mediante la asignación de valores numéricos al uso de palabras del galés en diferentes regiones sobre un mapa, pero con las limitaciones propias de la época, como la difícil asignación de caracteres especiales o la imposibilidad de administrar de grandes cantidades de texto (^[42]Hoch & Hayes, 2010). Años

Evaluando al candidato computer:


1) engl.: 10
2) ball: 10
3) purple: 7 (*)
4) orange: 6
5) errores: 5
6) lexical: 4 (*)
7) cylinder: 4
8) analizadores: 4
10) strike: 3
11) schulze: 3
12) automáticos: 3
14) animation: 3 (*)
15) ferreira: 3
18) learning: 3
19) blue: 3

computer
Lengua: eng
Frec: 184
Docs: 66
Nombre propio: 4 / 184 = 2%
Coocurrencias con glosario: 3
Frec. en corpus ref. en eng: 326
Puntaje: 3.840 = (3 + (1+6.16992500144231) / (1+7.53138146051631)));
Rechazado: muy común;

Referencias bibliográficas encontradas sobre cada término

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