We propose a supervised word sense disambiguation (WSD) system that uses features obtained from clustering results of word instances. Our approach is novel in that we employ semi-s...
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...
We describe here a method for automatically identifying word sense variation in a dated collection of historical books in a large digital library. By leveraging a small set of kno...
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...