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Update: February 24, 2023 The new version of Termout.org is now online, so this web site is now obsolete and will soon be dismantled. |
Lista de candidatos sometidos a examen:
1) ontology (*)
(*) Términos presentes en el nuestro glosario de lingüística
1) Candidate: ontology
Is in goldstandard
1
paper corpusSignosTxtLongLines359 - : Lenci, A. (2001). Building an Ontology for the Lexicon: Semantic types and word meaning . En P. Jensen & P. Skadhauge (Eds.), Ontolog y-Based Interpretation of Noun Phrases. Proceedings of the First International OntoQuery Workshop (pp.103-120). Kolding: Department of Business Communication and information Science, University of Southern Denmark. [ [39]Links ]
2
paper corpusSignosTxtLongLines375 - : Casanovas, P., Sartor, G., Biasiotti, M. A. & Fernández-Barrera, M. (2011). Theory and methodology in legal ontology engineering: Experiences and future directions . En G. Sartor, P. Casanovas, M. A. Biasiotti & M. Fernández-Barrera (Eds.), Approaches to legal ontologies, theories, domains, methodologies (pp. 3-14). Berlín-Heidelberg: Springer. [ [74]Links ]
3
paper corpusSignosTxtLongLines375 - : Velardi, P., Navigli, R., Cuchiarelli, A. & Neri, F. (2005). Evaluation of Ontolearn, a methodology for automatic population of domain ontologies. En P. Buitelaar, P. Cimiano & B. Magnini (Eds.), Ontology learning from text: Methods, evaluation and applications (pp . 92-106). Ámsterdam: IOS Press. [ [134]Links ]
4
paper corpusSignosTxtLongLines453 - : ^2 ^[165]Periñán and Mairal (2011) explain the methodology behind the creation of the Ontology of FunGramKB. For reasons of space, we shall only provide a brief explanation of this core component. The Ontology of FunGramKB is divided into three separate, albeit interrelated, subontologies in which the metaconcepts #ENTITIES, #EVENTS, and #QUALITIES respectively arrange in cognitive dimensions the following parts of speech: (i ) nouns, (ii) verbs, and (iii) adjectives. This type of organization stems from the fact that subsumption or IS-A is the only taxonomic relation permitted in the knowledge base.This contrasts with the approach adopted in FrameNet, for example, in which several frame-to-frame relations are posited (see ^[166]Ruppenhofer et al., 2010). These, however, have been shown to be problematic for NLP as far as reasoning is concerned (see ^[167]Ovchinnikova, Vieu, Oltramari, Borgo & Alexandrov, 2010). Conceptual, lexical and grammatical information is available through the NLP
5
paper corpusSignosTxtLongLines500 - : together with information related to their Thematic Roles (Theme, Location, etc.); the assignment of macrorole functions (Actor/Undergoer); the type of phrase that each variable represents (adjective phrase, adverb phrase, noun phrase, etc.); syntactic information (whether the phrase is an argument, argument adjunct or a nucleus which contains the predicate); specification of the prepositions that are introduced by a particular predicate (‘on’ in the case of the predicate ‘spread’) and any other selectional preferences that should be made explicit by using basic concepts from the Ontology, such as +SURFACE_00 for the location argument, as illustrated in [59]Figure 2, which shows the interface provided by the Grammaticon:
Evaluando al candidato ontology:
1) phrase: 5 (*)
3) predicate: 3 (*)
4) argument: 3 (*)
5) methodology: 3
ontology
Lengua: eng
Frec: 32
Docs: 11
Nombre propio: 4 / 32 = 12%
Coocurrencias con glosario: 3
Puntaje: 3.812 = (3 + (1+3.90689059560852) / (1+5.04439411935845)));
Candidato aceptado
Referencias bibliográficas encontradas sobre cada término
(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)| ontology |
: Buitelaar, P., Cimiano, P. & Magnini, B. (2007). Ontology learning from text: Methods, evaluation and applications. Amsterdam: IOS Press. : Cimiano, P. & Völker, J. (2005). Text2Onto: A framework for ontology learning and data-driven change discovery.Ponencia presentada en el 10th International Conference on Applications of Natural Language to Information Systems (pp. 227-238). Berlín-Heidelberg: Springer. : Cimiano, P. (2006). Ontology learning and population from text, algorithms, evaluation and applications. Nueva York: Springer. : Cimiano, P., Mädche, A., Staab, S. & Völker, J. (2009). Ontology learning. En S. Staab & R. Studer (Eds.), Handbook of ontologies (pp. 245-267). Berlín-Heidelberg: Springer. : Duranti, A. (2006). The social ontology of intentions. Discourse Studies, 8, 31-40. : Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199-220. : Imai, M. & Gentner, D. (1997). A cross-linguistic study of early word meaning: Universal ontology and linguistic influence. Cognition, 62, 169-200. : McCray, A. T. (2003). An Upper-level Ontology for the Biomedical Domain. Comparative And Functional Genomics, 4(1), 80-84. : Seddiqui, H. & Aono, M. (2010). Metric of intrinsic information content for measuring semantic similarity in an ontology. Proceedings of the 7th Asia-Pacific Conference on Conceptual Modeling (pp. 8996). Brisbane. : Van Valin, R. & Mairal, R. (2014). Interfacing the lexicon and an ontology in a linking system. In M. A. Gómez González, F. Ruiz de Mendoza Ibáñez & F. Gonzálvez-García (Eds.), Theory and practice in functional-cognitive space (pp. 205-228). Amsterdam: John Benjamins. |