Catálogo de publicaciones - libros

The German question answering (QA) system InSicht participated in QA@CLEF for the second time. It relies on complete sentence parsing, inferences, and semantic representation matching. This year, the system was improved in two main directions. First, the background knowledge was extended by large semantic networks and large rule sets. Second, linguistic processing was deepened by treating a phenomenon that appears prominently on the level of text semantics: coreference resolution. A new source of lexico-semantic relations and equivalence rules has been established based on compound analyses from document parses. These analyses were used in three ways: to project lexico-semantic relations from compound parts to compounds, to establish a subordination hierarchy for compounds, and to derive equivalence rules between nominal compounds and their analytic counterparts. The lack of coreference resolution in InSicht was one major source of missing answers in QA@CLEF 2004. Therefore the coreference resolution module CORUDIS was integrated into the parsing during document processing. The central step in the QA system InSicht, matching semantic networks derived from the question parse (one by one) with document sentence networks, was generalized. Now, a question network can be split at certain semantic relations (e.g. relations for local or temporal specifications). To evaluate the different extensions, the QA system was run on all 400 German questions from QA@CLEF 2004 and 2005 with varying setups. Some extensions showed positive effects, but currently they are minor and not statistically significant. The paper ends with a discussion why improvements are not larger, yet.

Joost is a question answering system for Dutch which makes extensive use of dependency relations. It answers questions either by table look-up, or by searching for answers in paragraphs returned by IR. Syntactic similarity is used to identify and rank potential answers. Tables were constructed by mining the CLEF corpus, which has been syntactically analyzed in full.

For our second participation to the Question Answering task of CLEF, we kept last year’s system named MUSCLEF, which uses two different translation strategies implemented in two modules. The multilingual module MUSQAT analyzes the French questions, translates “interesting parts”, and then uses these translated terms to search the reference collection. The second strategy consists in translating the question into English and applying QALC our existing English module. Our purpose in this paper is to analyze term translations and propose a mechanism for selecting correct ones. The manual evaluation of bi-terms translations leads us to the conclusion that the bi-term translations found in the corpus can confirm the mono-term translations.

We participated at four Question Answering tasks at CLEF 2005: the Italian monolingual (), Italian-English (), Bulgarian monolingual (), and Bulgarian-English () bilingual task. While we did not change the approach in the Italian task (), we experimented with several new approaches based on linguistic structures and statistics in the , , and tasks.

This paper describes the TALP-QA system in the context of the CLEF 2005 Spanish Monolingual Question Answering (QA) evaluation task. TALP-QA is a multilingual open-domain QA system that processes both factoid (normal and temporally restricted) and definition questions. The approach to factoid questions is based on in-depth NLP tools and resources to create semantic information representation. Answers to definition questions are selected from the phrases that match a pattern from a manually constructed set of definitional patterns.

This paper describes the work done by Priberam in the development of a question answering (QA) system for Portuguese. The system was built using the company’s natural language processing (NLP) workbench and information retrieval technology. Special focus is given to question analysis, document and sentence retrieval, as well as answer extraction stages. The paper discusses the system’s performance in the context of the QA@CLEF 2005 evaluation.

This paper describes a full data-driven system for question answering. The system uses pattern matching and statistical techniques to identify the relevant passages as well as the candidate answers for factoid and definition questions. Since it does not consider any sophisticated linguistic analysis of questions and answers, it can be applied to different languages without requiring major adaptation changes. Experimental results on Spanish, Italian and French demonstrate that the proposed approach can be a convenient strategy for monolingual and multilingual question answering.

We describe the extensions made to our 2004 QA@CLEF German/English QA-system, toward a fully German-English/English-German cross-language system with answer validation through web usage. Details concerning the processing of factoid, definition and temporal questions are given and the results obtained in the monolingual German, bilingual English-German and German-English tasks are briefly presented and discussed.

This paper describes the QUASAR Question Answering Information System developed by the RFIA group at the Departamento de Sistemas Informáticos y Computación of the Universidad Politécnica 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.

The University of Amsterdam participated in the Question Answering (QA) Track of CLEF 2005 with two runs. In comparison with previous years, our focus this year was adding to our multi-stream architecture a new stream that uses offline XML annotation of the corpus. We describe the new work on our QA system, present the results of our official runs, and note areas for improvement based on an error analysis.