This paper designs a novel lexical hub to disambiguate word sense, using both syntagmatic and paradigmatic relations of words. It only employs the semantic network of WordNet to c...
This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Although most of the ...
The degree of dominance of a sense of a word is the proportion of occurrences of that sense in text. We propose four new methods to accurately determine word sense dominance using...
We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic po...
This paper studies performance of various classifiers for Word Sense Disambiguation considering different training conditions. Our preliminary results indicate that the number and ...