This paper addresses the issue of word-sense ambiguity in extraction from machine-readable resources for the construction of large-scale knowledge sources. We describe two experim...
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
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...
We present an automatic method to disambiguate the senses of the near-synonyms in the entries of a dictionary of synonyms. We combine different indicators that take advantage of th...
The problem of recognizing textual entailment (RTE) has been recently addressed using syntactic and lexical models with some success. Here, we further explore this problem, this t...