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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) kernel (*)
(*) Términos presentes en el nuestro glosario de lingüística
1) Candidate: kernel
Is in goldstandard
1
paper corpusSignosTxtLongLines213 - : Ahora bien, la característica más relevante de la MVS es el uso de las funciones kernel (por ejemplo, lineal, polinominal, función de base radial, sigmoidal) para extender las aplicaciones de la determinación de separación óptima a casos no lineales (Christianini & Shaw-Taylor, 2002). Esto se hace traspasando los datos desde el espacio de entrada X a un amplio espacio de características X mediante una función O, y resolviendo el problema de aprendizaje lineal en X(0: X>X). La función real O no necesita ser conocida, es suficiente tener una función kernel k que calcule el producto interno en el espacio de características: k(x, y )=
2
paper corpusSignosTxtLongLines500 - : We have maintained the term ‘locative construction’ to exclusively refer to trivalent transitive constructions linked to putting verbs (such as the ‘spray/load class’) and also to some ‘removing’ verbs (such as ‘clear’, ‘clean’, ‘drain’ and ‘empty’), as exemplified in the following kernel constructs (examples (1) and (3)) and L1-constructions (examples (2) and (4)):^[85]^6
3
paper corpusSignosTxtLongLines500 - : The image impression construction might resemble ‘putting’ verbs in the locative construction since in both cases something is placed on a surface, but differs in the sense that with ‘creation’ verbs (e.g. ‘engrave’, ‘imprint’, ‘tattoo’, etc.), as a result of the event described by the verb, a new entity is created (i.e. a tattoo, an inscription, etc.). These verbs are ascribed to the Aktionsart active accomplishment, a type of event that is not changed by the construction. The kernel construct of these verbs in FunGramKB (exemplified in (11)) involves two arguments whose thematic roles, as explained in Section 1, are defined according to their metaconceptual distribution: a Theme, which in the metaconcept #CREATION is defined as the entity that creates another (‘members’ in example (11 )) and a Referent, conceived as the entity that is created by another entity (‘their initials’ in (11)). It is also common to find a prepositional phrase that should be analysed as an adjunct (an op
4
paper corpusSignosTxtLongLines500 - : The location subject construction also involves a change of Aktionsart class, since ‘fit’ verbs in the kernel construct are causative states (where we have an activity predicate causing a state: x does something that causes y be in z), whereas the L-1 construction codifies states with two arguments: the first argument position (‘a large cafeteria’ in (16 )) is a location argument whose capacity is specified by the second argument (‘300 people’ in (16)). In terms of macrorole assignment, and following the default Actor selection principle, the highest ranking argument in the logical structure must be assigned Actor (the participant responsible for the state of affairs, i.e. the logical subject), and the lowest ranking argument must be assigned Undergoer (the logical object in the state of affairs) following the Undergoer selection principle for default linking (^[95]Van Valin, 2005).
Evaluando al candidato kernel:
1) verbs: 7 (*)
2) argument: 5 (*)
5) entity: 4 (*)
7) logical: 3
kernel
Lengua: eng
Frec: 30
Docs: 6
Nombre propio: / 30 = 0%
Coocurrencias con glosario: 3
Puntaje: 3.894 = (3 + (1+4.32192809488736) / (1+4.95419631038688)));
Candidato aceptado
Referencias bibliográficas encontradas sobre cada término
(Que existan referencias dedicadas a un término es también indicio de terminologicidad.)| kernel |
: Además, se utilizó el kernel denominado Función de Base Radial (Radial Basis Function, RBF). Cabe señalar, que se ha seleccionado este kernel, pues, en general, se le reconoce una alta eficiencia en la etapa de entrenamiento (Colmenares, 2007). : Platt, J. (1998). Fast training of Support Vector Machines using sequential minimal optimization. Advances in Kernel Methods - Support Vector Learning. Cambridge: MIT Press. : Wang, T., Rao, J. & Hu, Q. (2014). Supervised word sense disambiguation using semantic diffusion kernel. Engineering Applications of Artificial Intelligence, 27, 167-174. |