Name
Abstract | ||
|---|---|---|
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 topic model to decompose this conditional probability into two conditional probabilities with latent variables. We propose three different instantiations 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. non-literal usages of potentially idiomatic expressions. In all three cases, we outperform state-of-the-art systems either quantitatively or statistically significantly. |
Year | Venue | Keywords |
|---|---|---|
2010 | ACL | best sense,probabilistic model,sense disambiguation problem,conditional probability,different instantiations,different degree,topic model,coarse-grained word sense disambiguation,sense disambiguation,token-based idiom detection,fine-grained word sense disambiguation,different task |
Field | DocType | Volume |
SemEval,Expression (mathematics),Conditional probability,Computer science,Latent variable,Artificial intelligence,Natural language processing,Statistical model,Topic model,Security token,Word-sense disambiguation,Machine learning | Conference | P10-1 |
Citations | PageRank | References |
34 | 1.35 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linlin Li | 1 | 117 | 7.66 |
| Benjamin Roth | 2 | 307 | 20.45 |
| Caroline Sporleder | 3 | 453 | 31.84 |