Taxonomies are an important resource for a variety of Natural Language Processing (NLP) applications. Despite this, the current stateof-the-art methods in taxonomy learning have d...
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...
A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an approach to automati...
We present a corpus{based approach to word{sense disambiguation that only requires information that can be automatically extracted from untagged text. We use unsupervised techniqu...
This paper presents a lexical model dedicated to the semantic representation and interpretation of individual words in unrestricted text, where sense discrimination is difficult t...