Multilinguality and Reversibility in Computational Semantic Lexicons

In this paper, we address the issue of generating multilingua.1 computational semantic lexicons from analysis lexicons, showing the necessity of relying on a conceptual lexicon. We first discuss the type of information which should be found in NLP lexicons, whatever their use (analysis, generation, speech, robotics). We claim that we should take advantage of the existing large-scale analysis lexicons and use tliem as the start ing point in the process of building large-scale generation lexicons, by first reversing tliem and then enhancing them. Tliis implies having access to a conceptual lexicon, which will serve as a pivot point between the analysis and the generation lexicons. We implemented the work reported here for Spanish and English MT projects, within the knowledge-based paradigm. From a theoretical point of view, regenerating the source text with the reversed analysis lexicon enabled us to enhance several issues as diverse as: evaluating analysis lexicons, testing the semantic analyser, evaluating which information should be added to the generation lexicon; and testing the grain-size of the pivot point between analysis and generation. I n t r o d u c t i o n There is no consensus on the type of lexicons which should be used for generators. It seems to depend on the type of generator. It also seems to depend on the kind of application involved: monolingual generation, naultilingual generation, machine translation: generation of sentences vs. texts vs. speech; or also generation from raw data vs. from conceptual representations built with generation in mind. Once one has an application in mind, then there are three main approaches one can adopt to build the lexicon: l e x i c o g r a p h i c : very at tractive for NLP applications at first sight, as they provide a useful description of the vocabulary; entries are distinguished on the basis of multiple senses and We would like to thank in the mikrokosmos team, Tom Herndon, . Jeff Longwel, Oscar Cossio and Javier Ochoa. subcategorisations. But in practice, this approach complicates the process of lexical disambiguation for parsing and lexical choice in generation by an unjustified proliferation of entries. s ta t i s t ica l : very attractive for NLP applications as it seems to replace knowledge-based approaches and therefore supplant the needs for human acquisition of large-scale semantic lexicons, which is a very time consuming task. However, the limits of statistical approaches haw: been pointed out by [Smadja, 1993]. Moreover, some phenomenon, such as event ellipsis (EE) cannot be handled by a pure statistical approach nor by a lexicographic approach, as its recovery necessitates a semantic t reatment , ([Viegas and Nirenburg, 1995a]), which is, in fact, handled by a computalional Linguistic approach making use of semantics. c o m p u t a t i o n a l l inguis t ic : the main advantage of this approach is that it is usually theoretically grounded, and is domainand application-independent. Moreover, it can handle phenomena which are out of the reach of other approaches and yet are necessary to enhance lexical choice in generation. For instance, the EE triggered by enjoy as in i) [ enjoyed the salmon very much, must be modeled with a semantic representation so that its recovery can be taken care of as in ii) I enjoyed eating . This is part of lex.ical choice as one can choose to realise the s y n t h e t i c version or the a n a l y t i c version of the EE, as exemplified in i) and ii) respectively (cf. [Viegas and Nirenburg, 1995a]). However necessary, adopting a linguistic approach is a difficult task, as one of the main drawbacks of this approach is that it is time consuming as far as the building of the lexicon is concerned. We address in next section how to bypass this drawback.