The success of machine learning approaches to word sense disambiguation (WSD) is
largely dependent on the representation of the context in which an ambiguous word occurs …

We describe a machine translation approach being designed at HKUST to integrate
semantic processing into statistical machine translation, beginning with entity and word …

Word sense disambiguation (WSD) is the problem of determining the right sense of a
polysemous word in a certain context. This paper investigates the use of unlabeled data for …

Many applications of natural language processing (NLP) need an accurate resolution of
various ambiguities existing in natural language. The task of fulfilling this need is also called …

As any other classification task, Word Sense Disambiguation requires a large number of
training examples. These examples, which are easily obtained for most of the tasks, are …

We present three generalisations of Kernel Principal Components Analysis (KPCA) which
incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA …

Thesis CSED 2008 Carpua Page 1 WORD SENSE DISAMBIGUATION FOR STATISTICAL
MACHINE TRANSLATION by MARINE JACINTHE CARPUAT A Thesis Submitted to The Hong …

Abstract Word Sense Disambiguation (WSD) is the problem of determining the right sense of
a polysemous word in a given context. In this paper, we will investigate the use of unlabeled …

In this paper we propose to use a semi-supervised learning algorithm to deal with word
sense disambiguation problem. We evaluated a semi-supervised learning algorithm, local …

One of the major problems in natural language processing (NLP) is the word sense
disambiguation (WSD) problem. It is the task of computationally identifying the right sense of …