The ACQUILEX LKB: representation issues in semi-automatic acquisition of large lexicons

We describe the lexical knowledge base system (LKB) which has been designed and implemented as part of the ACQUILEX project 1 to allow the representation of multilinguM syntactic and semantic information extracted from machine readable dictionaries (MRDs), in such a way that it is usable by natural language processing (NLP) systems. The LKB's lexical representation language (LRL) augments typed graph-based unification with default inheritance , formalised in terms of default unification of feature structures. We evaluate how well the LRL meets the practical requirements arising from the semi-automatic construction of a large scale, multilingual lexicon. The system as described is fully implemented and is being used to represent substantial amounts of information automatically extracted from MRDs. 1 Introduction The ACQUILEX LKB is designed to support representation of multilingual lexical information extracted from machine readable dictionaries (MRDs) in such a way that it can be utilised by NLP systems. In contrast to lexical database systems (LDBs) or thesaurus-like representations 1988) which represent extracted data in such a way as to support browsing and querying, our goal is to build a knowledge base which can be used as a highly struc-tured reusable lexicon, albeit one much richer in lexical semantic information than those commonly used in NLP. Thus, although we are using information which has been derived from MRDs (possibly after considerable processing involving some human intervention), our aim is not to represent the dictionary entries themselves. Our methodology is to store the dictionary entries and raw extracted data in our LDB (Carroll, 1990) and to use this information to build LKB entries which could be directly utilised by an NLP system. Briscoe (1991) discusses the LDB/LKB distinction in more detail and describes the ACQUILEX project as a whole. 1'The Acquisition of lexical knowledge for Natural Language Processing systems' (Esprit BRA-3030) Practical NLP systems need large lexicons. Even in cases such as database front ends, where the domain of the application is highly restricted, a practical natural language interface must be able to cope with an extensive vocabulary, in order to respond helpfully to a user who lacks domain knowledge, for example. For applications such as text-to-speech synthesis, interfaces to large-scale knowledge based systems, summarising and so on, large lexicons are clearly needed; for machine translation the requirement is for a large scale, multilingua] lexical resource. Acquisition of such information is a serious bottleneck in building NLP systems, and MRD sources currently …