Abstract Manually constructing a Wordnet is a difficult task, needing years of experts' time. As a first step to automatically construct full Wordnets, we propose approaches to generate Wordnet synsets for languages both resource-rich and resource-poor, using publicly available Wordnets, a

Manually constructing a Wordnet is a dif-ficult task, needing years of experts ’ time. As a first step to automatically construct full Wordnets, we propose approaches to generate Wordnet synsets for languages both resource-rich and resource-poor, us-ing publicly available Wordnets, a ma

Linked wordnets are invaluable linked lexical resources. Wordnet linking involves matching a particular synset (concept) in one wordnet to a synset in another wordnet. We have developed an automatic wordnet linking system that is divided into a number of stages. Starting with a synset in the first

Lexical knowledge bases (LKBs), such as WordNet, have been shown to be useful for a range of language processing tasks. Extending these resources is an expensive and time-consuming process. This paper describes an approach to address this problem by automatically generating a mapping from WordNet

Abstract: WordNet (WN) is a lexical knowledge base, first developed for English and then adopted for several Western European languages, which was created as a machinereadable dictionary based on psycholinguistic principles. Our paper is an attempt to discuss the semiautomatic generation of WNs

, temporal, and psychological aspects of everyday life. Whereas similar large-scale semantic knowledgebases like Cyc and WordNet are carefully handcrafted, ConceptNet is generated automatically from the 700,000 sentences of the Open Mind Common Sense Project -- a World Wide Web based collaboration with over

In what follows we will present a two-phase methodology for automatically building a wordnet (that we call target wordnet) strictly aligned with an already available wordnet (source wordnet). In the first phase the synsets for the target language are automatically generated and mapped onto

of categories. MCCA also scores texts in terms of social contexts that are similar to different functions of language. Distributions can be analyzed using non-agglomerative clustering to characterize the concepts and themes. MCCA categories have been mapped to WordNet senses. The defining characteristics

richer relational database that may improve natural language processing (NLP) technologies, such as word sense-disambiguation, and language-generation systems. In addition to merging the Dutch lexicons, the database is also mapped to a formal ontology to provide a more solid semantic backbone. Since

lexicons are difficult to construct requiring enormous amount of time and manpower. In this paper, we present a method for automatically generating multilingual Universal Word (UW) dictionaries (for English, Hindi and Marathi) from an input documentmaking use of English, Hindi and Marathi WordNets