Euskarazko denbora-egiturak etiketatzeko gidalerroak v1.0

To interpret the temporal information on texts, a mark-up language that will code that information is needed, in order to make that information automatically reachable. The most used mark-up language is TimeML (Pustejovsky et al., 2003), which has also been choosen for Basque. In this guidelines we present the Basque version of ISO-TimeML (ISO-TimeML working group, 2008). After having analysed the tags, attributes and values created for English, we describe the most appropriate ones to represent Basque time structures’ information.

Euskarazko denbora-egiturak etiketatzeko gidalerroak v2.0

[EN]To interpret the temporal information on texts, a mark-up language that will code that information is needed, in order to make that information automatically reachable. The most used mark-up language is TimeML (Pustejovsky et al., 2003), which has also been choosen for Basque. In this guidelines we present the Basque version of ISO-TimeML (ISO-TimeML working group, 2008). After having analysed the tags, attributes and values created for English, we describe the most appropriate ones to represent Basque time structures’ information.

ModeS TimeBank: un corpus TimeBank del español moderno

Con el objetivo de representar y analizar grandes cantidades de fuentes históricas textuales en un Sistema de Información Geográfica (SIG), se ha creado ModeS TimeBank. ModeS TimeBank es un corpus del español moderno (s. XVIII) anotado con información semántica temporal, eventiva y espacial, donde destaca el uso de los lenguajes de marcado TimeML y SpatialML. El corpus es además relevante no sólo por su datación e idioma sino por su dominio ya que está enmarcado en la temática de las redes de cooperación. El presente artículo pretende describir cómo se ha creado el corpus y qué criterios se han tenido en cuenta en su creación, además de señalar el alcance y las aplicaciones de ModeS TimeBank

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.