Catálogo de publicaciones - libros

Question Answering is a major research topic at the University of Alicante. For this reason, this year two groups participated in the QA@CLEF track using different approaches. In this paper we describe the work of group. This paper describes AliQAn, a monolingual open-domain Question Answering (QA) System developed in the Department of Language Processing and Information Systems at the University of Alicante for CLEF-2005 Spanish monolingual QA evaluation task. Our approach is based fundamentally on the use of syntactic pattern recognition in order to identify possible answers. Besides this, Word Sense Disambiguation (WSD) is applied to improve the system. The results achieved (overall accuracy of 33%) are shown and discussed in the paper.

Esfinge is a general domain Portuguese question answering system. It tries to take advantage of the steadily growing and constantly updated information freely available in the World Wide Web in its question answering tasks. The system participated last year for the first time in the monolingual QA track. However, the results were compromised by several basic errors, which were corrected shortly after. This year, Esfinge participation was expected to yield better results and allow experimentation with a Named Entity Recognition System, as well as try a multilingual QA track for the first time. This paper describes how the system works, presents the results obtained by the official runs in considerable detail, as well as results of experiments measuring the import of different parts of the system, by reporting the decrease in performance when the system is executed without some of its components/features.

This paper presents a question answering (QA) system called . approach to QA is based on question classification, semantic annotation and answer extraction pattern matching. performance is evaluated by conducting experiments in the following tasks: monolingual Finnish and French and bilingual Finnish-English QA. is the first system ever reported to perform monolingual textual QA in the Finnish language. This is also the task in which its performance is best: 23 % of all questions are answered correctly. performance in the monolingual French task is a little inferior to its performance in the monolingual Finnish task, and when compared to the other systems evaluated with the same data in the same task, its performance is near the average. In the bilingual Finnish-English task, was the only participating system, and – as is expected – its performance was inferior to those attained in the monolingual tasks.

Our second participation in CLEF-QA consited in six runs with Spanish as a target language. The source languages were Spanish, English an Italian. miraQA uses a simple representation of the question that is enriched with semantic information like typed Named Entities. Runs used different strategies for answer extraction and selection, achieving at best a 25’5% accuracy. The analysis of the errors suggests that improvements in answer selection are the most critical.

This paper describes the prototype developed in the Language Technologies Laboratory at INAOE for the Spanish monolingual QA evaluation task at CLEF 2005. The proposed approach copes with the QA task according to the type of question to solve (factoid or definition). In order to identify possible answers to factoid questions, the system applies a methodology centered in the use of lexical features. On the other hand, the system is supported by a pattern recognition method in order to identify answers to definition questions. The paper shows the methods applied at different stages of the system, with special emphasis on those used for answering factoid questions. Then the results achieved with this approach are discussed.

This paper describes the main components of the system built by the DLT Group at Limerick for participation in the QA Task at CLEF. The document indexing we used was again sentence-by-sentence but this year the Lucene Engine was adopted. We also experimented with retrieval query expansion using Local Context Analysis. Results were broadly similar to last year.

We present a report on our participation in the Indonesian-English question-answering task of the 2005 Cross-Language Evaluation Forum (CLEF). In this work we translated an Indonesian query set into English using a commercial machine translation tool called We used linguistic tools to find the answer to a question. The answer is extracted from a relevant passage and is identified as having the relevant tagging as the query.

This paper describes the architecture of a Bulgarian– Bulgarian question answering system — . The system relies on a partially parsed corpus for answer extraction. The questions are also analyzed partially. Then on the basis of the analysis some queries to the corpus are created. After the retrieval of the documents that potentially contain the answer, each of them is further processed with one of several additional grammars. The grammar depends on the question analysis and the type of the question. At present these grammars can be viewed as patterns for the type of questions, but our goal is to develop them further into a deeper parsing system for Bulgarian.

In this paper, we present the PRODICOS query answering system which was developed by the TALN team from the LINA institute. We present the various modules constituting our system and for each of them the evaluation is shown. Afterwards, for each of them, the evaluation is put forward to justify the results obtained. Then, we present the main improvement based on the use of semantic data.

This paper outlines efforts from the 2005 CLEF cross– language image retrieval campaign (ImageCLEF). Aim of the CLEF track is to explore the use of both text and content–based retrieval methods for cross–language image retrieval. Four tasks were offered in ImageCLEF: ad–hoc retrieval from an historic photographic collection, ad–hoc retrieval from a medical collection, an automatic image annotation task, and a user–centered (interactive) evaluation task. 24 research groups from a variety of backgrounds and nationalities (14 countries) participated in ImageCLEF. This paper presents the ImageCLEF tasks, submissions from participating groups and a summary of the main findings.