Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 88–99, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 88 Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation Rocco Tripodi Ca’ Foscari University of Venice rocco.tripodi@unive.it Roberto Navigli Sapienza University of Rome navigli@di.uniroma1.it Abstract Game-theoretic models, thanks to their intrin- sic ability to exploit contextual information, have shown to be particularly suited for the Word Sense Disambiguation task. They rep- resent ambiguous words as the players of a non-cooperative game and their senses as the strategies that the players can select in order to play the games. The interaction among the players is modeled with a weighted graph and the payoff as an embedding similarity func- tion, which the players try to maximize. The impact of the word and sense embedding rep- resentations in the framework was tested and analyzed extensively: experiments on standard benchmarks show state-of-art performances and different tests hint at the usefulness of using disambiguation to obtain contextualized word representations. 1 Introduction Word Sense Disambiguation (WSD), the task of linking the appropriate meaning from a sense in- ventory to words in a text, is an open problem in Natural Language Processing (NLP). It is particu- larly challenging because it deals with the seman- tics of words and, by their very nature, words are ambiguous and can be used with different mean- ings in different situations. Among the key tasks aimed at enabling Natural Language Understand- ing (Navigli, 2018), WSD provides a basic, solid contribution since it is able to identify the intended meaning of the words in a sentence (Kim et al., 2010). WSD can be seen as a classification task in which words are the objects to be classified and senses are the classes into which the objects have to be classified (Navigli, 2009); therefore it is possible to use supervised learning techniques to solve the WSD problem. One drawback with this idea is that it requires large amounts of data that are difficult to obtain. Furthermore, in the WSD context, the production of annotated data is even more complicated and excessively time- consuming compared to other tasks. This arises because of the variability in lexical use. Further- more, the number of different meanings to be con- sidered in a WSD task is in the order of thousands, whereas classical classification tasks in machine learning have considerably fewer classes. We decided to adopt a semi-supervised ap- proach to overcome the knowledge acquisition bottleneck and innovate the strand of research in- troduced by Tripodi and Pelillo (2017). These researchers developed a flexible game-theoretic WSD model that exploits word and sense simi- larity information. This combination of features allows the textual coherence to be maintained: in fact, in this model the disambiguation process is relational, and the sense assigned to a word must always be compatible with the senses of the words in the same text. It can be …

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