Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation Alexander Panchenkoz, Fide Martenz, Eugen Ruppertz, Stefano Faralliy, Dmitry Ustalov∗, Simone Paolo Ponzettoy, and Chris Biemannz zLanguage Technology Group, Department of Informatics, Universitat¨ Hamburg, Germany yWeb and Data Science Group, Department of Informatics, Universitat¨ Mannheim, Germany ∗Institute of Natural Sciences and Mathematics, Ural Federal University, Russia fpanchenko,marten,ruppert,
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[email protected] [email protected] Abstract manually in one of the underlying resources, such as Wikipedia. Unsupervised knowledge-free ap- Interpretability of a predictive model is proaches, e.g. (Di Marco and Navigli, 2013; Bar- a powerful feature that gains the trust of tunov et al., 2016), require no manual labor, but users in the correctness of the predictions. the resulting sense representations lack the above- In word sense disambiguation (WSD), mentioned features enabling interpretability. For knowledge-based systems tend to be much instance, systems based on sense embeddings are more interpretable than knowledge-free based on dense uninterpretable vectors. Therefore, counterparts as they rely on the wealth of the meaning of a sense can be interpreted only on manually-encoded elements representing the basis of a list of related senses. word senses, such as hypernyms, usage We present a system that brings interpretability examples, and images. We present a WSD of the knowledge-based sense representations into system that bridges the gap between these the world of unsupervised knowledge-free WSD two so far disconnected groups of meth- models. The contribution of this paper is the first ods. Namely, our system, providing access system for word sense induction and disambigua- to several state-of-the-art WSD models, tion, which is unsupervised, knowledge-free, and aims to be interpretable as a knowledge- interpretable at the same time.