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 I n t r o d u c t i o n 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 thesauruslike representations (e.g. Alshawi et al., 1989; Calzolari, 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 structured 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 largescale 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 seem the most promising source for semi-automatically acquiring the syntactic and semantic information needed. Previous work on extracting and representing syntactic information includes the work done on the Alvey Tools lexicon project (Carroll and Grover 1989) in which a large scale lexicon was produced semi-automatically from LDOCE (Longman Dictionary of Contemporary English, Procter, 1978) using a feature and unification based representation. There has been considerable discussion and some implementation of LKBs for the representation of semantic information extracted from MRD~, (e.g. Boguraev and Levin, 1990; Wilks et ai, 1989). However the knowledge representation languages assumed are rarely described formally; typically a semantic network or a frame representation has been suggested, but the interpretation and functionality of the links has been left vague. Several networks based on taxonomies have been built, and these are useful for tasks such as sensedisambiguation, but are not directly utilisable as NL1 c lexicons. For a reusable lexicon, a declarative, formallb specified, representation language is essential. In the ACQUILEX project we are concerned with the extraction and representation of both syntactic and lexical semantic information. A common representation language is needed, to allow the interaction of lexical semantic and syntactic properties to be described. Ther~ is currently a considerable amount of work in lexical semantics where unification based formalisms are used tc represent this interaction (e.g. Briscoe et al. 's (1990' account of logical metonymy (Pustejovsky, 1989, 1991) Sanfilippo's (1990) representation of thematic and aspec. tual information). However we also wish to structure th~ lexicon, in order to link lexical entries. This is essential