Background: Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information ret...
Abstract. A large class of unsupervised algorithms for Word Sense Disambiguation (WSD) is that of dictionary-based methods. Various algorithms have as the root Lesk's algorith...
Background: Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be u...
Toponym Disambiguation, i.e. the task of assigning to place name their correct reference in the world, is getting more attention from many researchers. Many methods have been prop...
Controlled natural languages (CNL) and computational semantics in general do not address word sense disambiguation, i.e., they tend to interpret only some functional words that are...
Abstract. The Robust-WSD at CLEF 2009 aims at exploring the contribution of Word Sense Disambiguation to monolingual and multilingual Information Retrieval. The organizers of the t...
Eneko Agirre, Giorgio Maria Di Nunzio, Thomas Mand...
In this paper, we describe a means for automatically building very large neural networks (VLNNs) from definition texts in machine-readable dictionaries, and demonstrate the use of...
In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge source...
This paper presents a method for word sense disambiguation and coherence understanding of prepositional relations. The method relies on information provided by
Multi-word terms are traditionally identified using statistical techniques or, more recently, using hybrid techniques combining statistics with shallow linguistic information. Al)...