Ángel F. Zazo

Reformulation of queries using similarity thesauri

One of the major problems in information retrieval is the formulation of queries on the part of the user. This entails specifying a set of words or terms that express their informational need. However, it is well-known that two people can assign different terms to refer to the same concepts. The techniques that attempt to reduce this problem as much as possible generally start from a first search, and then study how the initial query can be modified to obtain better results. In general, the construction of the new query involves expanding the terms of the initial query and recalculating the importance of each term in the expanded query. Depending on the technique used to formulate the new query several strategies are distinguished. These strategies are based on the idea that if two terms are similar (with respect to any criterion), the documents in which both terms appear frequently will also be related. The technique we used in this study is known as query expansion using similarity thesauri.

Experiments in Term Expansion Using Thesauri in Spanish

This paper presents some experiments carried out this year in the Spanish monolingual task at CLEF2002. The objective is to continue our research on term expansion. Last year we presented results regarding stemming. Now, our effort is centred on term expansion using thesauri. Many words that derive from the same stem have a close semantic content. However other words with very different stems also have semantically close senses. In this case, the analysis of the relationships between words in a document collection can be used to construct a thesaurus of related terms. The thesaurus can then be used to expand a term with the best related terms. This paper describes some experiments carried out to study term expansion using association and similarity thesauri.

Use of free on-line machine translation for interactive cross-language question answering

Free on-line machine translation systems are employed more and more by Internet users. In this paper we have explored the use of these systems for Cross-Language Question Answering, in two aspects: in the formulation of queries and in the presentation of information. Two topic-document language pairs were used, Spanish-English and Spanish-French. For each of these, two groups of users were created, depending on the level of reading skills in document language. When machine translation of the queries was used directly in the search, the number of correct answers was quite high. Users only corrected 8% of the translations proposed. As regards the possibility of using machine translation to translate into Spanish the text passages shown to the user, we expected the search of the users with little knowledge of the target language to improve notably, but we found that this possibility was of little help in finding the correct answers for the questions posed in the experiment.

Local query expansion using terms windows for robust retrieval

This paper describes our work at CLEF 2006 Robust task. This is an ad-hoc task that explores methods for stable retrieval by focusing on poorly performing topics. We have participated in all subtasks: monolingual (English, French, Italian and Spanish), bilingual (Italian to Spanish) and multilingual (Spanish to [English, French, Italian and Spanish]). In monolingual retrieval we have focused our effort on local query expansion, i.e. using only the information from retrieved documents, not from the complete document collection or external corpora, such as the Web. Some local expansion techniques were applied for training topics. Regarding robustness the most effective one was the use of co-occurrence based thesauri, which were constructed using co-occurrence relations in windows of terms, not in complete documents. This is an effective technique that can be easily implemented by tuning only a few parameters. In bilingual and multilingual retrieval experiments several machine translation programs were used to translate topics. For each target language, translations were merged before performing a monolingual retrieval. We also applied the same local expansion technique. In multilingual retrieval, weighted max-min normalization was used to merge lists. In all the subtasks in which we participated our mandatory runs (using title and description fields of the topics) obtained very good rankings. Runs with short queries (only title field) also obtained high MAP and GMAP values using the same expansion technique.

Web page retrieval by combining evidence

The participation of the REINA Research Group in WebCLEF 2005 focused in the monolingual mixed task. Queries or topics are of two types: named and home pages. For both, we first perform a search by thematic contents; for the same query, we do a search in several elements of information from every page (title, some meta tags, anchor text) and then we combine the results. For queries about home pages, we try to detect using a method based in some keywords and their patterns of use. After, a re-rank of the results of the thematic contents retrieval is performed, based on Page-Rank and Centrality coeficients.

Interactive and bilingual question answering using term suggestion and passage retrieval

The Question Answering Task requires user interaction. Users can help the system by reformulating the questions, adding information to them or selecting the documents on which the system should work to obtain the answers. Our group has researched the effects on user interaction of suggesting terms to be added to the question, and the differences between using fragments or complete documents. This article describes the experiments we carried out and discusses the results we obtained.