Most probabilistic classi ers used for word-sense disambiguationhave either been based on onlyone contextual feature or have used a model that is simply assumed to characterize th...
Word Sense Disambiguation (WSD) is an intermediate task that serves as a means to an end defined by the application in which it is to be used. However, different applications have...
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...
In this paper, word sense dismnbiguation (WSD) accuracy achievable by a probabilistic classifier, using very milfimal training sets, is investigated. \Ve made the assuml)tiou that...
We investigate the task of unsupervised constituency parsing from bilingual parallel corpora. Our goal is to use bilingual cues to learn improved parsing models for each language ...