Estela Saquete Boró

On the identification of temporal clauses

This paper describes a machine learning approach to the identification of temporal clauses by disambiguating the subordinating conjunctions used to introduce them. Temporal clauses are regularly marked by subordinators, many of which are ambiguous, being able to introduce clauses of different semantic roles. The paper also describes our work on generating an annotated corpus of sentences embedding clauses introduced by ambiguous subordinators that might have temporal value. Each such clause is annotated as temporal or non-temporal by testing whether it answers the questions when, how often or how long with respect to the action of its superordinate clause. Using this corpus, we then train and evaluate personalised classifiers for each ambiguous subordinator, in order to set apart temporal usages. Several classifiers are evaluated, and the best performing ones achieve an average accuracy of 89.23% across the set of ambiguous connectives.

University of Alicante at WiQA 2006

This paper presents the participation of University of Alicante at the WiQA pilot task organized as part of the CLEF 2006 campaign. For a given set of topics, this task presupposes the discovery of important novel information distributed across different Wikipedia entries. The approach we adopted for solving this task uses Information Retrieval, query expansion by feedback, novelty re-ranking, as well as temporal ordering. Our system has participated both in the Spanish and English monolingual tasks. For each of the two participations the results are promising because, by employing a language independent approach, we obtain scores above the average. Moreover, in the case of Spanish, our result is very close to the best achieved score. Apart from introducing our system, the present paper also provides an in-depth result analysis, and proposes future lines of research, as well as follow-up experiments.