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Retrieval of Spanish-English Bilingual Information by Means of a Standard Automatic Translation System

This paper describes our participation in bilingual retrieval (queries in Spanish on documents in English), by means of an information retrieval system based on the vector model. The queries, formulated in Spanish, were translated into English by means of a commercial automatic translation system; the terms extracted from the resulting translations were filtered in order to get rid of empty words and then they were normalised by stemming. Results are poorer than those obtained through monolingual retrieval with the original queries in English slightly above 15%. Introduction Our participation in CLEF 2000 is centred mainly on bilingual retrieval, by which we mean queries in Spanish against a collection of documents in English. Obviously, we have worked with the very same queries formulated originally in English, which we have used to obtain a line of comparable results. When one tries to solve queries in a given language versus documents written in a different one, the problems is to get a homogeneous representation of both queries and documents, so that they can be compared and thus allowing us to establish a measure of similarity between them [OARD96]. Once this homogeneous representation has been obtained, the similarity between a query and each of the documents of the collection may be computed by means of any of the systems usually employed for monolingual retrieval. For term-based information-retrieval systems, as is the case of the vector model [SALTON83], the question is to insure that the terms that represent documents and queries use the same language. One way or another, this involves some sort of translation; at least in principle, translation of queries seems to be less expensive than translating whole documents. Anyway, the problem is the translation of individual terms, which seems less complex than translating a sintactically structured text. The main problem, beyond using a machine-readable bilingual dictionary, is to disambiguate those terms: each of them may have different meanings and each of them will have a different equivalent in the other language. It is not easy to determine the proper equivalents for each case and several methods have been proposed with this purpose [AGIRRE2000]; final results depend, to a large extent, of the quantity and quality of semantic knowledge contained in the lexicons and dictionaries employed . An obvious alternative to approach the problem of bilingual retrieval is to use some automatic translation system; there is quite a number of commercially available systems. However, these systems are not too well liked, since in general terms the translations they produce contain many mistakes and, occasionally, are not acceptable from a linguistic point of view. It must be noticed, however, that the linguistic requirements of retrieval systems are rather lower than those of the persons who must read and understand the translations [HULL96]. In fact, many information-retrieval systems do not use or consider syntactic constructs and, when terms experience some kind of normalisation process, they ignore morphology. The utilisation of one of these automatic translation commercial systems poses no difficulties and, in our case, lacking experience in bilingual retrieval, seems to be a good way to start on this subject.