In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach

This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. The method relies on the use of the wide-coverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a Conceptual Density formula developed for this purpose. This fully automatic method requires no hand coding of lexical entries, hand tagging of text nor any kind of training process. The results of the experiments have been automatically evaluated against SeroCot, the sense-tagged version of the Brown Corpus.

The absence of training data is a real problem for corpus-based approaches to sense disambiguation, one that is unlikely to be solved soon. Selectional preference is traditionally connected with sense ambiguity; this paper explores how a statistical model of selectionai preference, requiring neither manual annotation of selection restrictions nor supervised training, can be used in sense disambiguation.

The automatic disambiguation of word senses has been an interest and concern since the earliest days of computer treatment of language in the 1950's. Sense disambiguation is an “intermediate task ” (Wilks and Stevenson, 1996) which is not an end in itself, but rather is necessary at one level or another to accomplish most natural language processing tasks. It is

Resnik and Yarowsky (1997) made a set of observations about the state of the art in automatic word sense disambiguation and, motivated by those observations, offered several specific proposals regarding improved evaluation criteria, common training and testing resources, and the definition of sense inventories. Subsequent discussion of those proposals resulted in senseval, the first evaluation exercise for word sense disambiguation (Kilgarriff and Palmer forthcoming). This article is a revised and extended version of our 1997 workshop paper, reviewing its observations and proposals and discussing them in light of the senseval exercise. It also includes a new in-depth empirical study of translingually-based sense inventories and distance measures, using statistics collected from native-speaker annotations of 222 polysemous contexts across 12 languages. These data show that monolingual sense distinctions at most levels of granularity can be effectively captured by translations into some ...

In this paper we describe the SSI algorithm, a structural pattern matching algorithm for WSD. The algorithm has been applied to the gloss disambiguation task of Senseval-3. 1

This paper presents a method for the resolution of lexical ambiguity and its automatic evaluation over the Brown Corpus. The method relies on the use of the wide-coverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a Conceptual Density formula developed for this purpose. This fully automatic method requires no hand coding of lexical entries, hand tagging of text nor any kind of training process. The results of the experiment have been automatically evaluated against SemCor, the sense-tagged version of the Brown Corpus.

In this paper, we report recent improvements to the exemplar-based learning approach for word sense disambiguation that have achieved higher disambiguation accuracy. By using a larger value of k, the number of nearest neighbors to use for determining the class of a test example, and through 10-fold cross validation to automatically determine the best k, we have obtained improved disambiguation accuracy on a large sense-tagged corpus first used in (Ng and Lee, 1996). The accuracy achieved by our improved exemplar-based classifier is comparable to the accuracy on the same data set obtained by the Naive-Bayes algorithm, which was reported in (Mooney, 1996) to have the highest disambiguation accuracy among seven state-of-the-art machine learning algorithms.

This paper deals with two important ambiguities of natural language: prepositional phrase attachment and word sense ambiguity. We propose a new supervised learning method for PPattachment based on a semantically tagged corpus. Because any sufficiently big sense-tagged corpus does not exist, we also propose a new unsupervised context based word sense disambiguation algorithm which amends the training corpus for the PP attachment by word sense tags. We present the results of our approach and evaluate the achieved PP attachment accuracy in comparison with other methods.

This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Although most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full-fledged lexical ambiguity resolution should com- bine several information sources and tech- niques. The set of techniques have been applied in a combined way to disambiguate the genus terms of two machine-readable dictionaries (MRD), enabling us to construct complete taxonomies for Spanish and French. Tested accuracy is above 80% overall and 95% for two-way ambiguous genus terms, showing that taxonomy building is not limited to structured dictionaries such as LDOCE.