This paper summarises a set of methodologies and techniques for the fast construction of multilingual WordNets. The

We present a robust approach for linking already existing lexi- cal/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select among a set of candidates the node in a target taxonomy that bests matches each node in a source tax- onomy. In particular, we use it to map the nominal part of WordNet 1.5 onto WordNet 1.6, with a very high precision and a very low remaining ambiguity.

Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification.

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from pre-existing lexical resources. We present a new and robust approach for linking already existing lexical/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select --among all the candidate translations proposed by a bilingual dictionary-- the right English WordNet synset for each sense in a taxonomy automatically derived from a Spanish monolingual dictionary. Although on average, there are 15 possible WordNet connections for each sense in the taxonomy, the method achieves an accuracy over 80%. Finally, we also propose several ways in which this technique could be applied to enrich and improve existing lexical databases. 1 Introduction There is an increasing need of having available general, accurate and broad coverage multilingual lexical/semantic resources for developing nl applications. Thus, a very active field inside nl during the last years has ...

We describe a robust approach for linking already existing lexical/semantic hierarchies. We use a constraint satisfaction algorithm (relaxation labelling) to select --among a set of candidates-- the node in a target taxonomy that bests matches each node in a source taxonomy. In this paper we present the complete mapping of the nominal, verbal, adjectival and adverbial parts of WordNet 1.5 onto WordNet 1.6.

This paper presents the automatic construction of a Korean WordNet from pre-existing lexical resources. A set of automatic WSD techniques is described for linking Korean words collected from a bilingual MRD to English WordNet synsets.

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from preexisting lexical resources. This paper presents a new approach for linking already existing hierarchies. The Relaxation labeling algorithm is used to select --among all the candidate connections proposed by a bilingual dictionary-- the right conection for each node in the taxonomy. 1 Introduction There is no doubt about the increasing need of owning accurate and broad coverage general lexical/semantic resources for developing NL applications. Thus, one of the main issues in last years as regards NLP activities has been focused on the fast development of generic language resources. These resources include lexicons, lexical databases (ldbs), lexical knowledge bases (lkbs), ontologies, etc. Special interest presents, for knowledge-based NLP tasks, the availability of wide coverage ontologies. Most known ontologies (suchas gum, cyc, Ontos, Microkosmos, edr or WordNet, see [G'o98] for an extens...

Sharing knowledge across projects is often considered impossible, especially if the systems involved present differences in underlying theory, representation, or even programming language. This paper, taking a collaboration between the CAT2 and PANGLOSS MT projects as an example, demonstrates that it is possible to combine resources developed separately to support the processing of different modules in a joint system. Despite the differences in the two MT methods, the transfer-based CAT2 and the interlingual PANGLOSS systems proved to share similarities in their linguistic representations that simplified the interfacing between the two systems. Referring to this result the paper presents a discussion of the transfer/interlingua continuum and attempts to bridge the gap between the two MT alternatives. 1

In this paper we face the automatic construction of a lexical taxonomy for the Spanish language using as input a taxonomy of English (WordNet) and a set of bilingual (English/Spanish) resources.

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from preexisting lexical resources. First, a set of automatic and complementary techniques for linking Spanish words collected from monolingual and bilingual MRDs to English WordNet synsets are described. Second, we show how resulting data provided by each method is then combined to produce a preliminary version of a Spanish WordNet with an accuracy over 85%. The application of these combinations results on an increment of the extracted connexions of a 40% without losing accuracy. Both coarsegrained (class level) and fine-grained (synset assignment level) confidence ratios are used and evaluated. Finally, the results for the whole process are presented. 1 Introduction There is no doubt about the increasing importance of using wide coverage ontologies for NLP tasks. Although available ontologies (Upper Model (Bateman 90), CYC (Lenat 95), WordNet (Miller 90), ONTOS (Nirenburg & Defrise 93), Mikrokosm...