The selectional preferences of verbal predicates are an important component of a computational lexicon. They have frequently been cited as being useful for wsd, alongside other sources of knowledge. We evaluate automatically acquired selectional preferences on the level playing eld provided by senseval to examine to what extent they help in WSD.

In this paper, we have presented a detailed overview of the Word Sense disambiguation (WSD) efforts undertaken in India related to Indian Languages. Also in remaining sections we have discussed the method used by us for Marathi LanguageWSD. This approach of WSD uses combination of Rules to disambiguate a word to provide a suitable sense with respect to the context.

Current state-of-the-art Word Sense Disambiguation (WSD) algorithms are mostly supervised and use the P (Sense|Word) statistic for annotation. This P (Sense|Word) statistic is obtained after training the model on an annotated corpus. The performance of WSD algorithms do not match the efficiency and quality of human annotation. It is therefore important to know the role of the contextual clues i...

This paper presents the participation of the semantic N-levels search engine SENSE at the CLEF 2009 Ad Hoc Robust-WSD Task. During the participation at the same task of CLEF 2008, SENSE showed that WSD can be helpful to improve retrieval, even though the overall performance was not exciting mainly due to the adoption of a pure Vector Space Model with no heuristics. In this edition, our aim is t...

BACKGROUND Annual influenza epidemics occur worldwide resulting in considerable morbidity and mortality. Spreading pattern of influenza is not well understood because it is often hampered by the quality of surveillance data that limits the reliability of analysis. In Japan, influenza is reported on a weekly basis from 5,000 hospitals and clinics nationwide under the scheme of the National Infec...

Word-Sense Disambiguation (WSD), holds promise for many NLP applications requiring broad-coverage language understanding, such as summarization (Barzilay and Elhadad, 1997) and question answering (Ramakrishnan et al., 2003). Recent studies have also shown that WSD can benefit machine translation (Vickrey et al., 2005) and information retrieval (Stokoe, 2005). Much work has focused on the comput...

Previous algorithms to compute lexical chains suffer either from a lack of accuracy in word sense disambiguation (WSD) or from computational inefficiency. In this paper, we present a new lineartime algorithm for lexical chaining that adopts the assumption of one sense per discourse. Our results show an improvement over previous algorithms when evaluated on a WSD task.

What: We like minimally supervised learning (bootstrapping). Let’s convert it to unsupervised learning (“strapping”). How: If the supervision is so minimal, let’s just guess it! Lots of guesses lots of classifiers. Try to predict which one looks plausible (!?!). We can learn to make such predictions. Results (on WSD): Performance actually goes up! (Unsupervised WSD for translational senses, Eng...