This article presents a new model for word sense disambiguation formulated in terms of evolutionary game theory, where each word to be disambiguated is represented as a node on a graph whose edges represent word relations and senses are represented as classes. The words simultaneously update their class membership preferences according to the senses that neighboring words are likely to choose. We use distributional information to weigh the influence that each word has on the decisions of the others and semantic similarity information to measure the strength of compatibility among the choices. With this information we can formulate the word sense disambiguation problem as a constraint satisfaction problem and solve it using tools derived from game theory, maintaining the textual coherence. The model is based on two ideas: Similar words should be assigned to similar classes and the meaning of a word does not depend on all the words in a text but just on some of them. The article provides an in-depth motivation of the idea of modeling the word sense disambiguation problem in terms of game theory, which is illustrated by an example. The conclusion presents an extensive analysis on the combination of similarity measures to use in the framework and a comparison with state-of-the-art systems. The results show that our model outperforms state-of-the-art algorithms and can be applied to different tasks and in different scenarios.
Other unsupervised and semi-supervised approaches, instead of computing the prevalent sense of a word, try to identify the actual sense of a word in a determined phrase, exploiting the information derived from its context. This is the case of traditional algorithms, which exploit the pairwise semantic similarity among a target word and the words in its context @cite_95 @cite_74 @cite_93 . Our work could be considered as a continuation of this tradition, which tries to identify the intended meaning of a word given its context, using a new approach for the computation of the sense combinations.
@cite_95 @cite_93 @cite_74
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Veronis (2004) has recently proposed an innovative unsupervised algorithm for word sense disambiguation based on small-world graphs called HyperLex. This paper explores two sides of the algorithm. First, we extend Veronis' work by optimizing the free parameters (on a set of words which is different to the target set). Second, given that the empirical comparison among unsupervised systems (and with respect to supervised systems) is seldom made, we used hand-tagged corpora to map the induced senses to a standard lexicon (WordNet) and a publicly available gold standard (Senseval 3 English Lexical Sample). Our results for nouns show that thanks to the optimization of parameters and the mapping method, HyperLex obtains results close to supervised systems using the same kind of bag-of-words features. Given the information loss inherent in any mapping step and the fact that the parameters were tuned for another set of words, these are very interesting results. 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. Word sense induction is an unsupervised task to find and characterize different senses of polysemous words. This work investigates two unsupervised approaches that focus on using distributional word statistics to cluster the contextual information of the target words using two different algorithms involving latent dirichlet allocation and spectral clustering. Using a large corpus for achieving this task, we quantitatively analyze our clusters on the Semeval-2010 dataset and also perform a qualitative analysis of our induced senses. Our results indicate that our methods successfully characterized the senses of the target words and were also able to find unconventional senses for those words. Word sense induction aims to discover different senses of a word from a corpus by using unsupervised learning approaches. Once a sense inventory is obtained for an ambiguous word, word sense discrimination approaches choose the best-fitting single sense for a given context from the induced sense inventory. However, there may not be a clear distinction between one sense and another, although for a context, more than one induced sense can be suitable. Graded word sense method allows for labeling a word in more than one sense. In contrast to the most common approach which is to apply clustering or graph partitioning on a representation of first or second order co-occurrences of a word, we propose a system that creates a substitute vector for each target word from the most likely substitutes suggested by a statistical language model. Word samples are then taken according to probabilities of these substitutes and the results of the co-occurrence model are clustered. This approach outperforms the other systems on graded word sense induction task in SemEval-2013.