Emilio Sanchis Arnal

A passage retrieval system for multilingual question answering

In this paper we present a new method to improve the coverage of Passage Retrieval (PR) systems when these systems are employed for the Question Answering (QA) tasks. The ranking of passages obtained by the PR system is rearranged to emphasize those passages with more probability to contain the answer. The new ranking is based on finding the n-gram structures of the question that are presented in the passage, and the weight of the passages increases when they contain longer n-grams structures of the question. The results we present show that the application of this method improves notably the coverage of the classical PR system based on the Space Vectorial Model.

Using the wordnet ontology in the GeoCLEF geographical information retrieval task

This paper describes how we managed to use the WordNet ontology for the GeoCLEF 2005 English monolingual task. Both a query expansion method, based on the expansion of geographical terms by means of WordNet synonyms and meronyms, and a method based on the expansion of index terms, which exploits WordNet synonyms and holonyms. The obtained results show that the query expansion method was not suitable for the GeoCLEF track, while WordNet could be used in a more effective way during the indexing phase.

QUASAR: the question answering system of the universidad politécnica de valencia

This paper describes the QUASAR Question Answering Information System developed by the RFIA group at the Departamento de Sistemas Informaticos y Computacion of the Universidad Politecnica of Valencia for the 2005 edition of the CLEF Question Answering exercise. We participated in three monolingual tasks: Spanish, Italian and French, and in two cross-language tasks: Spanish to English and English to Spanish. Since this was our first participation, we focused our work on the passage-based search engine while using simple pattern matching rules for the Answer Extraction phase. As regards the cross-language tasks, we had to resort to the most common web translation tools.

Verb sense disambiguation using support vector machines: impact of wordnet-extracted features

The disambiguation of verbs is usually considered to be more difficult with respect to other part-of-speech categories. This is due both to the high polysemy of verbs compared with the other categories, and to the lack of lexical resources providing relations between verbs and nouns. One of such resources is WordNet, which provides plenty of information and relationships for nouns, whereas it is less comprehensive with respect to verbs. In this paper we focus on the disambiguation of verbs by means of Support Vector Machines and the use of WordNet-extracted features, based on the hyperonyms of context nouns.

Cluster analysis of railway directory inquire dialogs

Cluster analysis of dialogs with transport directory service allows revealing the typical scenarios of dialogs, which is useful for designing automatic dialog systems. We show how to parameterize dialogs and how to control the process of clustering. The parameters include both data of transport service and features of passenger s behavior. Control of clustering consists in manipulating the parameter s weights and checking stability of the results. This technique resembles Makagonov s approach to the analysis of dweller s complaints to city administration. We shortly describe B. Stein s new MajorClust method and demonstrate its work on real person-to-person dialogs provided by Spanish railway service.