Word sense disambiguation has always been a key problem in Natural Language Processing. In the paper, we use the method of Information Gain to calculate the weight of different po...
We present results that show that incorporating lexical and structural semantic information is effective for word sense disambiguation. We evaluated the method by using precise in...
Takaaki Tanaka, Francis Bond, Timothy Baldwin, San...
Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web ...
In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowl...
This paper presents Domain Relevance Estimation (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respe...