The use of topical features is abundant in Natural Language Processing (NLP), a major example being in dictionary-based Word Sense Disambiguation (WSD). Yet previous research does...
When a word sense disambiguation (WSD) system is trained on one domain but applied to a different domain, a drop in accuracy is frequently observed. This highlights the importance...
This paper presents a probabilistic model for sense disambiguation which chooses the best sense based on the conditional probability of sense paraphrases given a context. We use a...
Sense-tagged corpora are used to evaluate word sense disambiguation (WSD) systems. Manual creation of such resources is often prohibitively expensive. That is why the concept of p...
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...