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 propose three different instanti-ations of the model for solving sense disambiguation problems with different degrees of resource availability.

The proposed models are tested on three different tasks: coarse-grained word sense disambiguation, fine-grained word sense disambiguation , and detection of literal vs. nonliteral usages of potentially idiomatic expressions.

Finally, we test our model on the related sense disambiguation task of distinguishing literal and nonliteral usages of potentially ambiguous expressions such as break the ice.

Sense Paraphrases For word sense disambiguation tasks, the paraphrases of the sense keys are represented by information from WordNet 2.1.

Word sense disambiguation (WSD) is the task of automatically determining the correct sense for a target word given the context in which it occurs.

Recently, several researchers have experimented with topic models (Brody and Lapata, 2009; Boyd-Graber et al., 2007; Boyd-Graber and Blei, 2007; Cai et al., 2007) for sense disambiguation and induction.

Previous approaches using topic models for sense disambiguation either embed topic features in a supervised model (Cai et al., 2007) or rely heavily on the structure of hierarchical lexicons such as WordNet (Boyd-Graber et al., 2007).

Recently, a number of systems have been proposed that make use of topic models for sense disambiguation .

3.2 The Sense Disambiguation Model

While pseudo-words originally evaluated word sense disambiguation , they are now commonly used to evaluate selectional preferences.

Pseudo-words were introduced simultaneously by two papers studying statistical approaches to word sense disambiguation (WSD).

One way to mitigate this problem is with pseudo-words, a method for automatically creating test corpora without human labeling, originally proposed for word sense disambiguation (Gale et al.,

This idea originates from a similarity-based word sense disambiguation method developed by Karov and Edelman (1998).

To reflect two distinct aspects of the phenomenon, metaphor annotation can be split into two stages: identifying metaphorical senses in text (akin word sense disambiguation ) and annotating source — target domain mappings underlying the production of metaphorical expressions.

Such annotation can be viewed as a form of word sense disambiguation with an emphasis on metaphoricity.