Distributions of the senses of words are often highly skewed. This fact is exploited by word sense disambiguation (WSD) systems which back off to the predominant (most frequent) s...
Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In ...
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 explores the large-scale acquisition of sense-tagged examples for Word Sense Disambiguation (WSD). We have applied the "WordNet monosemous relatives" method t...