Minimal Commitment and Full Lexical Disambiguation: Balancing Rules and Hidden Markov Models

In: Proceedings o/CoNLL-2000 and LLL-2000, pages 111-114, Lisbon, Portugal, 2000. Minimal Commitment and Full Lexical Disambiguation: Balancing Rules and Hidden Markov Models Patrick Ruch and Robert Baud and Pierrette Bouillon and Gilbert Robert* Medical Informatics Division, University Hospital of Geneva ISSCO, University of Geneva {ruch, baud}@dim.hcuge.ch, {bouillon, robert}@issco.unige.ca Abstract In this paper we describe the construction of a part-of-speech tagger both for medical doc- ument retrieval purposes and XP extraction. Therefore we have designed a double system: for retrieval purposes, we rely on a rule-based ar- chitecture, called minimal commitment, which is likely to be completed by a data-driven tool (HMM) when full disambiguation is necessary. 1 Introduction Nowadays, most medical information is stored in textual documents 1, but such large amount of data may remain useless if retrieving the rel- evant information in a reasonable time becomes impossible. Although some large-scale informa- tion retrieval (IR) evaluations, made on unre- stricted corpora (Hersh and al., 1998), and on medical texts (Hersh, 1998), are quite critical towards linguistic engineering, we believe that natural language processing is the best solution to face two major problems of text retrieval en- gines: expansion of the query and lexical dis- ambiguation. Disambiguation can be separated between MS (morpho-syntactic, i.e. the part-of- speech (POS) and some other features) and WS (word-sense) disambiguation. Although we aim at developing a common architecture for pro- cessing both the MS and the WS disambigua- tion (Ruch and al., 1999), this paper focuses on the MS tagging. • We would like to thank Thierry Etchegoyhen and Erik Tjong Kim Sang for their helpful assistance while writing this paper. The Swiss National Fundation supported the present study. 1While our studies were made on French corpora, the examples are provided in English -when possible- for the sake of clarity. 2 Background Before starting to develop our own MS tagger, some preliminary studies on general available systems were conducted; if these studies go far beyong the scope of this paper, we would like to report on the main conclusions. Both statis- tical taggers (HMM) and constraint-based sys- tems were assessed. Two guidelines were fram- ing the study: performances and minimal com- mitment. We call minimal commitment 2 the property of a system, which does not attempt to solve ambiguities when it is not likely to solve it well! Such property seems important for IR purposes, where we might prefer noise rather than silence in the recall process. However, it must remain optional, as some other tasks (such as the NP extraction, or the phrase chunking (Abney, 1991)) may need a full disambiguation. 2.1 Data-driven tools We adapted the output of our morphological analyser for tagging purposes (Bouillon et al., 1999). We trained and wrote manual biases for an HMM tagger, but results were never far above 97% (i.e. about 3% of error); with an av- erage ambiguity level of around 16%, it means that almost 20% of the ambiguities were at- tributed a wrong tag! We attempted to set a confidence threshold, so that …

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