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This article describes the first release version of a new lexicostatistical database of Northern Eurasia, which includes Europe as the most well-researched linguistic area. Unlike in other areas of the world, where databases are restricted to covering a small number of concepts as far as possible based on often sparse documentation, good lexical resources providing wide coverage of the lexicon are available even for many smaller languages in our target area. This makes it possible to attain near-completeness for a substantial number of concepts. The resulting database provides a basis for rich benchmarks that can be used to test automated methods which aim to derive new knowledge about language history in underresearched areas.

We discuss an aspect of an affect-detection system used in edrama by intelligent conversational agents, namely affective interpretation of limited sorts of metaphorical utterance. Our system currently only deals with cases, which we found to be quite common in edrama, in which a person is compared to, or stated to be, something non-human such as an animal, object, artefact or supernatural being. Our approach permits a limited degree of variability and extension of these metaphors. We discuss how these metaphorical utterances are recognized, how they are analysed and their affective content determined and in particular how the electronic lexical database, WordNet, and the natural language glosses of the WordNet sysnsets can be used. We also discuss how this relatively shallow approach relates in important ways to the deeper ATT-Meta theory of metaphor interpretation and to approaches to affect and emotion in metaphor theory. We finish by illustrating the approach with a number of ‘worked examples’.

This paper addresses one of the workshop’s themes namely that ‘an understanding of how emotion is expressed and perceived in language is not complete without addressing the role of figurative language and metaphor as basic scaffolding or tool for modulating affective text content.’ It falls into two overlapping halves: the metaphorical perception or construction of emotion in English; and the metaphorical expression of emotion. Data is taken from a lexical database of metaphors, and gives an overview and selected examples of metaphor themes’ contribution to the construction and expression of emotion.

Grammar-lexis rules and relations ensuring correct insertion of major lexical entries (nouns, verbs and deverbals) play an essential part in the computational morphology of Arabic. This chapter, which is based on the experiences of the DIINAR.1 Arabic lexical resource and related software, and on that of the first version of the SYSTRAN Arabic-English MT system, outlines previous approaches of the computational morphology of the language (Section 2): root and pattern (shortly recalled); lexeme-based; machine learning and statistical; stems, based on roots and patterns, and finally, the stem-based approach, including root and pattern as well as grammar-lexis information. The latter, which is the most compliant to the requirements of machine-translation and other high-level applications, is further developed in Section 3 of the Arabic word-form and a mapping of rules and relations accounting for grammar-lexis relations operating within the boundaries of that complex unit. In the Word-Formatives Grammar, rules and relations involving the lexical nucleus of the word-form play a crucial part and are formalised in a computational perspective. The stem either coincides with, or is the core of the nucleus, because lexical entries include two overall categories: in the first, stem and entry coincide; in the second, the lexical entry corresponds to a morphological compound encompassing the stem and a lexicalized extension (in most cases, a suffix which is part of the entry). Correct relations between the lexical nucleus and the other formatives included in the word-form are ensured through morphosyntactic specifiers associated to each entry of the lexical database. These relations, which have been included in the DIINAR.1 database, are both finite in number and exhaustive in coverage. They also allow computational morphology and other applications to rely on a good restriction of the generated lexica: only cliticized or affixed formatives that can effectively be associated with a given lexical nucleus are added, and ‘illegal’ ones are ruled out. In the DIINAR.1 resource, the effective number of inflected word-forms is 7,774,938 (about nine times less than one would obtain through ‘blind’ generation). A comprehensive mapping of examples is given. Their compatibility with applications going beyond computational morphology is also outlined

This paper describes a heuristic approach to automatic acquisition of contextual representations of senses in a machine-readable dictionary (MRD). Including contextual information in an MRD-based lexical database offers several benefits. First, the representation can be used to merge closely related senses and construct a coarser sense division, so unnecessarily fine sense distinctions can be avoided in word sense disambiguation (WSD). The contextual information can also be used as a knowledge base to develop a WSD system. Furthermore, if the algorithms run on several MRDs, the contextual representation also provides a means of linking relevant senses across multiple MRDs to create an integrated lexical database. The algorithms are based primarily on information retrieval techniques to build a list of topics that are most relevant to the definition of each MRD sense. An implementation of the method using definition sentences in the Longman Dictionary of Contemporary English is described. To this end, the topical word lists and topical cross-references in the Longman Lexicon of Contemporary English are used. We have conducted a series of experiments and evaluations to assess the performance of the proposed approach.

We give a brief outline of the design of the English lexical database WordNet. The meanings of nouns, verbs, adjectives, and adverbs are represented in terms of their relations to other (groups of) lexical items, with which they are interlinked by means of conceptual-semantic and lexical relations. A relational approach to the lexicon is particularly useful in studying discrepancies between the conceptual and the lexical inventory of a language and detecting lexical gaps. We compare WordNet’s semantic classification of the verb lexicon with a syntactically-driven approach like the one undertaken by Levin (1993). These two approaches do not always overlap in terms of yielding the same verb classes, but the relational approach, which structures verbs primarily into hierarchies and allows for nodes occupied by non-lexicalized concepts, can provide for more fine-grained structures that reconcile both semantic and syntactic classifications.1

We give a brief outline of the design and contents of the English lexical database WordNet, which serves as a model for similarly conceived wordnets in several European languages. WordNet is a semantic network, in which the meanings of nouns, verbs, adjectives, and adverbs are represented in terms of their links to other (groups of) words via conceptual-semantic and lexical relations. Each part of speech is treated differently reflecting different semantic properties. We briefly discuss polysemy in WordNet, and focus on the case of meaning extensions in the verb lexicon. Finally, we outline the potential uses of WordNet not only for applications in natural language processing, but also for research in stylistic analyses in conjunction with a semantic concordance.

We discuss ways in which EuroWordNet (EWN) can be used in multilingual information retrieval activities, focusing on two approaches to Cross-Language Text Retrieval that use the EWN database as a large-scale multilingual semantic resource. The first approach indexes documents and queries in terms of the EuroWordNet Inter-Lingual-Index, thus turning term weighting and query/document matching into language-independent tasks. The second describes how the information in the EWN database could be integrated with a corpus-based technique, thus allowing retrieval of domain-specific terms that may not be present in our multilingual database. Our objective is to show the potential of EuroWordNet as a promising alternative to existing approaches to Cross-Language Text Retrieval.

In this paper the linguistic design of the database under construction within the EuroWordNet project is described. This is mainly structured along the same lines as the Princeton Word-Net, although some changes have been made to the WordNet overall design due to both theoretical and practical reasons. The most important reasons for such changes are the multilinguality of the EuroWordNet database and the fact that it is intended to be used in Language Engineering applications. Thus, i) some relations have been added to those identified in WordNet; ii) some labels have been identified which can be added to the relations in order to make their implications more explicit and precise; iii) some relations, already present in the WordNet design, have been modified in order to specify their role more clearly.

This paper describes two fundamental aspects in the process of building of the EuroWordNet database. In EuroWordNet we have chosen for a flexible design in which local wordnets are built relatively independently as language-specific structures, which are linked to an Inter-Lingual-Index (ILI). To ensure compatibility between the wordnets, a core set of common concepts has been defined that has to be covered by every language. Furthermore, these concepts have been classified via the ILI in terms of a Top Ontology of 63 fundamental semantic distinctions used in various semantic theories and paradigms. This paper first discusses the process leading to the definition of the set of Base Concepts, and the structure and the rationale of the Top Ontology.

This paper describes IITLEX, a lexical database under development at Illinois Institute of Technology. It emphasizes the sources of the data and the organization of the database.

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