WordNet is an on-line lexical reference system whose design is inspired by current

We describe the lexical knowledge base sys- tem (LKB) which has been designed and implemented as part of the ACQUILEX project x to allow the representation of multilingual syn- tactic 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 unifi- cation 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.

The paper focuses on the description of the approach, taken within the ESPRIT BRA project ACQUILEX, towards: i) acquisition of semantic information from several machine- readable dictionaries (in four languages), and ii) its representation in a common Lexical Knowledge Base. Knowledge extraction is guided by a) empirical observations and b) theoretical hypotheses. As for representation, we stress the convergence of a) and b) towards the possibility of organizing the information extracted from MRDs in the form of 'meaning types' or 'templates', where a common meta-language is used to encode conceptual and relational information. Examples taken from two Italian monolingual dictionaries and from LDOCE are given. Different uses of these templates (e.g. as guides in the semantic analysis of the definitions, as a structure for comparing, unifying, merging, integrating information coming from different sources and different languages, as a tool for correcting 'incoherences' in dictionaries, etc.) are described.

This thesis aims at building a Lexical Knowledge Base (LKB) that will be useful to a Natural Language Processing (NLP) system by extracting information from a Machine Readable Dictionary (MRD). Our source of knowledge is the American Heritage First Dictionary (AHFD) which contains 1800 entries and is designed for children of age six to eight learning the structure and the basic vocabulary of their language. Using a children's dictionary allows us to restrict our vocabulary, but still work on general knowledge about day to day concepts and actions. Our Lexical Knowledge Base contains information extracted from the AHFD and represented using the Conceptual Graph (CG) formalism. The graph definitions explicitly give the information contained in all the noun and verb definitions from the AHFD. Each sentence of each definition is tagged, parsed and automatically transformed into a conceptual graph. The type hierarchy, extracted automatically from the definitions, groups all the nouns a...

this dissertation was the attempt to find mathematical models for the semantic relations in lexical databases such as Roget's International Thesaurus (RIT, 1962) and WordNet. This lead to a detailed analysis of lexical and conceptual structures within linguistic data (Chapter 1), to the creation of Relational Concept Analysis as an extension of Formal Concept Analysis (Chapter 2), and to the formal modeling of semantic relations (Chapter 3). The purpose of this research is to provide a set of formal representation techniques that allow a structural approach to knowledge organisation and representation systems. Applications can be found in computerized systems that customize natural language storage and processing, in relational databases, semantic networks, conceptual knowledge systems as developed by cognitive scientists, library classification systems, thesauri, and others. Chapter 4 demonstrates how Relational Concept Analysis interlinks with some other theories in this area. It further indicates a wide range of applications of Relational Concept Analysis in addition to the lexical databases for which the theory originally was designed

This paper explores the relationship between WordNet and other conventional lingnistically-based lexical resources. We introduce an algorithm for align- ing word senses from different resources, and use it in our experiment to sketch the role played by WordNet, as far as sense discrimination is concerned, when put in the context of other lexical databases. The results show how and where the resources systematically differ from one another with respect to the degree of polysemy, and suggest how we can (i) overcome the inadequacy of individual resources to achieve an overall balanced degree of sense discrimination, and (ii) use a combination of semantic clas- siftcation schemes to enrich lexical information for NLP.

This paper presents an algorithm to integrate dif-ferent lexical resources, through which we hope to overcome the individual inadequacy of the resources, and thus obtain some enriched lexical semantic in-formation for applications such as word sense disam-biguation. We used WordNet as a mediator between a conventional dictionary and a thesaurus. Prelimi-nary results support our hypothesised structural re-lationship, which enables the integration, of the re-sources. These results also suggest that we can con> bine the resources to achieve an overall balanced de-gree of sense discrimination. 1

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