We present a multilingual joint approach to Word Sense Disambiguation (WSD). Our method exploits BabelNet, a very large multilingual knowledge base, to perform graphbased WSD across different languages, and brings together empirical evidence from these languages using ensemble methods. The results show that, thanks to complementing wide-coverage multilingual lexical knowledge with robust graph-...

In spite of decades of research on word sense disambiguation (WSD), all-words general purpose WSD has remained a distant goal. Many supervised WSD systems have been built, but the effort of creating the training corpus annotated sense marked corpora has always been a matter of concern. Therefore, attempts have been made to develop unsupervised and knowledge based techniques for WSD which do not...

Word Sense Disambiguation confronts with the lack of syntagmatic information associated to word senses. In the present work we propose a method for the enrichment of EuroWordNet with syntagmatic information, by means of the WSD process itself. We consider that an ambiguous occurrence drastically reduces its ambiguity when considered together with the words it establishes syntactic relations in ...

ion measure quantity time_period 4 6 invention instrumentation product is-a links Key: ATCM without WSD ATCM with first sense WSD heuristic a) object slot for ‘begin’ b) object slot for ‘produce’

One of the most important issues in the field of Natural Language Engineering is Word Sense Disambiguation (WSD).Gurumukhi or more commonly known as Punjabi, is world’s 12th most widely spoken language and this language is morphologically rich. But surprisingly, there are relatively less efforts in the field of computerization and development of lexical resources of this language. It is therefo...

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words...

The document describes the knowledgebased Domain-WSD system using heuristic rules (knowledge-base). This HRWSD system delivered the best performance (55.9%) among all Chinese systems in SemEval-2010 Task 17: All-words WSD on a specific domain.

The present paper explores a wide range of word sense disambiguation (WSD) algorithms for German. These WSD algorithms are based on a suite of semantic relatedness measures, including path-based, information-content-based, and gloss-based methods. Since the individual algorithms produce diverse results in terms of precision and thus complement each other well in terms of coverage, a set of comb...

Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context and successful approaches are known to benefit many applications in Natural Language Processing. Although, supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words...