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.
@cite_13: This paper presents a comprehensive empirical exploration and evaluation of a diverse range of data characteristics which influence word sense disambiguation performance. It focuses on a set of six core supervised algorithms, including three variants of Bayesian classifiers, a cosine model, non-hierarchical decision lists, and an extension of the transformation-based learning model. Performance is investigated in detail with respect to the following parameters: (a) target language (English, Spanish, Swedish and Basque); (b) part of speech; (c) sense granularity; (d) inclusion and exclusion of major feature classes; (e) variable context width (further broken down by part-of-speech of keyword); (f) number of training examples; (g) baseline probability of the most likely sense; (h) sense distributional entropy; (i) number of senses per keyword; (j) divergence between training and test data; (k) degree of (artificially introduced) noise in the training data; (l) the effectiveness of an algorithm's confidence rankings; and (m) a full keyword breakdown of the performance of each algorithm. The paper concludes with a brief analysis of similarities, differences, strengths and weaknesses of the algorithms and a hierarchical clustering of these algorithms based on agreement of sense classification behavior. Collectively, the paper constitutes the most comprehensive survey of evaluation measures and tests yet applied to sense disambiguation algorithms. And it does so over a diverse range of supervised algorithms, languages and parameter spaces in single unified experimental framework.
Unsupervised and knowledge-based algorithms for WSD are attracting great attention from the research community. This because, supervised systems require training data, which are difficult to obtain. In fact, producing sense tagged data is a time-consuming process, which has to be carried out separately for each language of interest. Furthermore, as investigated by Yarowsky and Florian , the performances of a supervised algorithm degrade substantially with the increasing of sense entropy. Sense entropy refers to the distribution over the possible senses of a word, as seen in training data. Additionally, a supervised system has problems to adapt to different contexts, because it depends on prior knowledge, which makes the algorithm rigid, therefore can not efficiently adapt to domain specific cases, when other optimal solution may be available @cite_13 .