This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints -- that words tend to have one sense per discourse and one sense per collocation -- exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96%.

This paper describes a program that disambiguates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's categories serve as approximations of conceptual classes. The categories listed for a word in Roget's index tend to correspond to sense distinctions; thus selecting the most likely category provides a useful level of sense disambiguation. The selection of categories is accomplished by identifying and weighting words that are indicative of each category when seen in context, using a Bayesian theoretical framework. Other

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

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It is well-known that there are polysemous words like sentence whose "meaning " or "sense " depends on the context of use. We have recently reported on two new word-sense disambiguation systems, one trained on bilingual material (the Canadian Hansards) and the other trained on monolingual material (Roget's Thesaurus and Grolier's Encyclopedia). As this work was nearing completion, we observed a very strong discourse effect. That is, if a polysemous word such as sentence appears two or more times in a well-written discourse, it is extremely likely that they will all share the same sense. This paper describes an experiment which confirmed this hypothesis and found that the tendency to share sense in the same discourse is extremely strong (98%). This result can be used as an additional source of constraint for improving the performance of the word-sense disambiguation algorithm. In addition, it could also be used to help evaluate disambiguation algorithms that did not make use of the discourse constraint.

Most probabilistic classifiers used for word-sense disambiguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies among multiple contextual features. In this paper, a different approach to formulating a probabilistic model is presented along with a case study of the performance of models produced in this manner for the disambiguafion of the noun interest. We describe a method for formulating probabilistic models that use multiple contextual features for word-sense disambiguafion, without requiring untested assumptions regarding the form of the model. Using this approach, the joint distribution of all variables is described by only the most systematic variable interactions, thereby limiting the number of parameters to be estimated, supporting computational efficiency, and providing an understanding of the data.

Lexical ambiguity is a pervasive problem in natural language processing. However, little quantitative information is available about the extent of the problem, or about the impact that it has on information retrieval systems. We report on an analysis of lexical ambiguity in information retrieval test collections, and on experiments to determine the utility of word meanings for separating relevant from non-relevant documents. The experiments show that there is considerable ambiguity even in a specialized database. Word senses

It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval (IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will increase. However, recent research into the application of a word sense disambiguator to an IR system failed to show any performance increase. From these results it has become clear that more basic research is needed to investigate the relationship between sense ambiguity, disambiguation, and IR. Using a technique that introduces additional sense ambiguity into a collection, this paper presents research that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an IR system when it is retrieving from very short queries. In addition we argue that if a word sense disambiguator is to be of any use to an IR system, the disambiguator must be abl...

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

Word sense disambiguation has been recognized as a major problem in natural language processing research for over forty years. Much of this work has been stymied by difficulties in acquiring appropriate lexical resources, such as semantic networks and annotated corpora. Following the suggestion in Brown et al. (1991a) and Dagan et al. (1991), we have achieved considerable progress recently by taking advantage of a new source of testing and training materials. Rather than depending on small amounts of hand-labeled text, we have been making use of relatively large amounts of parallel text, text such as the Canadian Hansards (parliamentary debates), which are available in two (or more) languages. The translation can often be used in lieu of hand-labeling. For example, consider the polysemous word sentence, which has two major senses: (1) a judicial sentence, and (2), a syntactic sentence. We can collect a number of sense (1) examples by extracting instances that are translated as peine, and we can collect a number of sense (2) examples by extracting instances that are translated as phrase. In this way, we have been able to acquire a considerable amount of testing and training material for developing and testing our disambiguation algorithms. The availability of this testing and training material has enabled us to develop quantitative disambiguation methods that achieve 90 % accuracy in discriminating between two very distinct senses of a noun such as