Knowledge plays a central role in intelligent systems. Manual knowledge acquisition is very inefficient and expensive. In this paper, we present (1) an automatic method to acquire...
Ping Chen, Wei Ding 0003, Chris Bowes, David Brown
This paper presents a new approach for combining different semantic disambiguation methods that are part of a Word Sense Disambiguation(WSD) system. The way these methods are comb...
Recent research works on unsupervised word sense disambiguation report an increase in performance, which reduces their handicap from the respective supervised approaches for the sa...
George Tsatsaronis, Iraklis Varlamis, Kjetil N&osl...
Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available ope...
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...