A robust category guesser for Dutch medical language

A robust category guesser for Dutch medical language Peter Spyns Katholieke Universiteit Leuven University Hospital Gasthuisberg Division of Medical Informatics Herestraat 49, B-3000 Leuven peter©uz, kuleuven, ac. be Abstract In this paper, we want to describe the ar- chitecture and some of the implementation issues of a large scale category guesser for Dutch medical vocabulary. We Mso provide numerical data on the precision and cover- age of this category guesser, which has to cover for the moment only the vocabulary of the cardiology domain. The category guesser uses non-morphologic information (endstring matching) as well as truly mor- phologic knowledge (inflection, derivation and compounding). Since we deal with a sublanguage some linguistic features are easier to handle (Grishman and Kittredge, 1986), (Sager et al., 1987). Subsequently we will describe in detail the differents parts which interact to successfully identify unknown medical words. 1 Introduction 1.1 NLP in medicine Medical patient reports consists mainly of free text, combined with results of various laboratories. While numerical data can easily be stored and processed for archiving and research purposes, free text is rather difficult to be treated by computer, although it con- tains the most relevant information. Several authors put forward the hypothesis that Natural Language Processing (NLP) and Knowledge Representation (KR) of medical discharge summaries have become the key-issues in the domMn of intelligent medical in- formation processing (Baud et al., 1992), (Gabrieli and Speth, 1987), (McCray, 1991). However, only a few NLP-driven systems have actually been imple- mented (Friedman and Johnson, 1992) . For Dutch, a limited prototype has been developed (Spyns, 1991), (Spyns and Adriaens, 1992). A broader sys- tem covering a larger part of the Dutch grammar and medical vocabulary is currently under development• 150 This activity forms part of the MENELAS-project 1 • This project comprises a morphological, syntactic, semantic and pragmatic analysis of the medical sub- language for Dutch, English and French (Spyns et al., 1992). The project also focuses on Knowledge Representation (by means of Conceptual Graphs) (Sowa, 1984), (Volot et al., 1993) and Production Systems (Bouaud and Zweigenbaum, 1992). 1.2 The Category Guesser for Dutch Medical Language This paper focuses on the morphological and lex- ical component of the system, which is a combi- nation of a database application and a Prolog rule interpreter. This component is already functioning and is used continuously during the current exten- sion of the coverage of the Dutch grammar (Spyns et al., 1993). The importance of morphologic analy- sis of medical vocabulary has been widely recognised (Wingert, 1985), (Wolff, 1984), (Dujols et al., 1991), (Pacak and Pratt, 1969) (Pacak and Pratt, 1978), (Norton, 1983)• In the following sections, we will describe the dif- ferent parts which interact to identify the word forms of a given sentence• The various stages of the anal- ysis of the word forms are described• A major dis- tinction can be made between forms "known by the system" (= stored in the dictionary cf. section 2) and unknown forms whose linguistic characteristics need to be computed …

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