Comparing Corpora And Lexical Ambiguity

In this paper we compare two types of corpus, focusing on the lexical mnbiguity of each of them. The first corpns consists mainly of newspaper articles and Hterature excerpts, while the second belc)ngs to the medical domain. To conduct the study, we have used two different disambiguation tools. However, first of all, we must verify the performance of each system in its respective application domain. We then use these systems in order to assess and compare both the general ambiguity rate and the particularities of each domain. (mantitative results show that medical documents are lexically less ambiguous than tmrestrieted documents. Our conclusions show the importance of the application area in the design of NLP tools. Introduction and background Although some large-scale evaluations carried out on unrestricted texts (Hersh 1998a, SparkJones 1999), and even on medical documents (Hersh 1998b), conclude in a quite critical way about using NLP tools for information retrieval, we believe that such tools are likely to solve some lexical ambiguity issues. We also believe that some special settings -particular to the application areamust be taken into account while developing such NLP tools. Let us recall two major problems while retrieving documents with NLP engines (Salton, 1988): 1-Expansion: the user is generally as interested in retrieving documents with exactly the same words, as in retrieving documents with semantically related words (synonyms, generics, specifics...). Thus, a query based on the word liver, should be able to retrieve documents containing words such as hepatic. This expansion process is usually thesaurus-based. The thesaurus can be built manually or automatically (as, for ex~ple , in Nazarenko, 1997). 2-Disambiguation: a search based on tokens may retrieve irrelevant documents since tokens are often lexically ambiguous. Thus, face can refer to a body part, as a noun, or an action, as a verb. Finally, this latter problem may be split into two sub problems. The disambiguafion task can be based on parts-of-speech (POS) or word-sense (WS) information, but the chronological relation is still a discussion within the community. Although, the target of our work (Ruch and al., 1999, Bouillon and al., 2000) is a free-grained semantic disambiguation of medical texts for IR purposes, we believe that the POS disambiguation is an important preliminary step. Therefore this paper focuses on POS tagging, and compares morpho-syntacfic lexical ambiguities (MSLA) in medical texts to MSLA in unrestricted corpora. Although the results of the study conform to preliminary naive expectations, the method is quite original I. Most of the comparative studies, dedicated to corpora, have addressed the problem by applying metrics on words entities or word pieces (as in studies working with nI We do not claim to be pioneer in the domain, as others authors (Biber 1998, Folch and al., 2000) axe exploring similar metrics. However, it is interesting to notice that for these authors the adaptation of the NLP tools has rarely been questioned in a technical point-of view, and in order to feed back the design of NLP systems.