A Model for Preference

A MODEL FOR PREFERENCE Dominique Petitpierre ISSCO University of Geneva 54 route des Acacias CH-1227 Geneva, Switzerland Steven Krauwer Louis des Tombe Instituut voor Algemene Taalwetenschap Rijksuniversiteit Utrecht Trans 14 3512 JK Utrecht, The Netherlands Doug Arnold Centre for Cognitive Studies University of Essex Colchester, CO4 3SQ, England Giovanni B. Varile DG XIII, Batiment Jean Monnet Commission of the European communities P.O. Box 1907, Luxembourg, Luxembourg Abstract In this paper we address the problem of choosing the best solution(s) from a set of interpretations of the same object (in our case a segment of text). A notion of preference is stated, based on pairwise comparisons of complete interpretations in order to obtain a partial order among the competing interpretations. An experimental implementation is described, which uses Prolog-like preference statements. 1. Introduction In this paper we address the problem of choosing the best solution(s) from a set of interpretations of the same text seg- ment (For the sake of brevity, throughout this text we use the term interpretation, where in fact we should write representa- tion of an interpretation). Although developed in the context of a machine translation system (the Eurotra project, Arnold 1986, Arnold and des Tombe 1987), we believe that our approach is suited to many other fields of computational linguistics and even outside (pattern recognition, etc.). After a brief overview of the problem (section 2), we suggest a general method to deal with preference (section 3) and then describe a possible implementation (section 4). An appendix gives actual examples of preference statements. 2. What is preference? In the computational linguistics literature, the term 'preference' has been used in different contexts. We shall men- tion a few, selectively, (in section 2.1 which may be skipped) and then state our own view (in section 2.2). 2.1. Various approaches Preference strategies have often been used for dealing with the problem of ill- formed input (a particular case of robust- ness, cf below section 2.2) (AJCL 1983, Charniak 1983). Following Weischedel and Sondheimer (1983) we distinguish the cases 134 where preference is part of the particular computation being performed (Wilks 1973, Fass and Wilks 1983, Pereira 1985) from the case where it is a separate process, run after the results of the computation have been obtained (Jensen et al 1983, Weischedel and Sondheimer 1983). A frequent approach to preference is scoring. A numeric score is calculated, independently, for each competing interpretation and is then used to rank the interpretations. The best interpreta- tions are then chosen. The score can be the number of constraints satisfied by the interpretation (Wilks 1973, Fass & Wilks 1983), where these constraints might be assigned relative weights by the linguist (Robinson 1982, Charniak 1983, Bennett and Slocum 1985) or calculated by the computer (Papegaaij 1986). Such techniques have been used extensively for speech recogni- tion (Paxton 1977, Walker et al 1978) and in the field of expert systems (such as Mycin, Buchanan & Shortliffe 1984), where the calculation of both score and ranking become quite complex with …

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