  This paper 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 paper
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.
