text: This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word to a known sense definition based solely on the values of automatically identifiable features in text. These methods and feature sets are found to be more successful in disambiguating nouns rather than adjectives or verbs. Overall, the most accurate of these procedures is McQuitty's similarity analysis in combination with a high dimensional feature set.
  @cite_37: The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to find a single model that adequately characterizes the data in a training sample. However, during model selection a sequence of models is generated that consists of the best-fitting model at each level of model complexity. The Naive Mix utilizes this sequence of models to define a probabilistic model which is then used as a probabilistic classifier to perform word-sense disambiguation. The models in this sequence are restricted to the class of decomposable log-linear models. This class of models offers a number of computational advantages. Experiments disambiguating twelve different words show that a Naive Mix formulated with a forward sequential search and Akaike's Information Criteria rivals established supervised learning algorithms such as decision trees (C4.5), rule induction (CN2) and nearest-neighbor classification (PEBLS).
  @cite_14: 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 disambiguation of the noun "interest". We describe a method for formulating probabilistic models that use multiple contextual features for word-sense disambiguation, 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.
  @cite_6: The three corpus-based statistical sense resolution methods studied here attempt to infer the correct sense of a polysemous word by using knowledge about patterns of word cooccurrences. The techniques were based on Bayesian decision theory, neural, networks, and content vectors as used in information retrieval. To understand these methods better, we posed a very specific problem: given a set of contexts, each containing the noun line in a known sense, construct a classifier that selects the correct sense of line for new contexts. To see how the degree of polysemy affects performance, results from three- and six-sense tasks are compared.The results demonstrate that each of the techniques is able to distinguish six senses of line with an accuracy greater than 70 . Furthermore, the response patterns of the classifiers are, for the most part, statistically indistinguishable from one another. Comparison of the two tasks suggests that the degree of difficulty involved in resolving individual senses is a greater performance factor than the degree of polysemy.
  @cite_3: In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation. We tested our WSD program, named Lexas , on both a common data set used in previous work, as well as on a large sense-tagged corpus that we separately constructed. Lexas achieves a higher accuracy on the common data set, and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of WordNet .
  @cite_24: Previous work [Gale, Church and Yarowsky, 1992] showed that with high probability a polysemous word has one sense per discourse. In this paper we show that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation. We test this empirical hypothesis for several definitions of sense and collocation, and discover that it holds with 90--99 accuracy for binary ambiguities. We utilize this property in a disambiguation algorithm that achieves precision of 92 using combined models of very local context.
  @cite_2: 
  @cite_5: This paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques. The specific problem tested involves disambiguating six senses of the word line'' using the words in the current and proceeding sentence as context. The statistical and neural-network methods perform the best on this particular problem and we discuss a potential reason for this observed difference. We also discuss the role of bias in machine learning and its importance in explaining performance differences observed on specific problems.
  @cite_16: Statistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted in order to find the model that best characterizes the interactions among features. This paper expands existing model selection methodology and presents the first comparative study of model selection search strategies and evaluation criteria when applied to the problem of building probabilistic classifiers for word-sense disambiguation.
  @cite_13: A number of researchers in text processing have independently observed that people can consistently determine in which of several given senses a word is being used in text, simply by examining the half dozen or so words just before and just after the word in focus. The question arises whether the same task can be accomplished by mechanical means. Experimental results are presented which suggest an affirmative answer to this query. Three separate methods of discriminating English word senses are compared information-theoretically. Findings include a strong indication of the power of domain-specific content analysis of text, as opposed to domain-general approaches.
summary: Word--sense disambiguation has more commonly been cast as a problem in supervised learning (e.g., @cite_13 , @cite_2 , @cite_24 , @cite_6 , @cite_14 , @cite_5 , @cite_3 , @cite_16 , @cite_37 ). However, all of these methods require that manually sense tagged text be available to train the algorithm. For most domains such text is not available and is expensive to create. It seems more reasonable to assume that such text will not usually be available and attempt to pursue unsupervised approaches that rely only on the features in a text that can be automatically identified.