An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages

An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages Dmitry Ustalov∗†, Denis Teslenko†, Alexander Panchenko‡, Mikhail Chernoskutov†, Chris Biemann‡, Simone Paolo Ponzetto∗ ∗ Data and Web Science Group, University of Mannheim, Germany † Ural Federal University, Russia ‡ Universit¨at Hamburg, Department of Informatics, Language Technology Group, Germany {dmitry,simone}@informatik.uni-mannheim.de, teslenkoden@gmail.com, mikhail.chernoskutov@urfu.ru, {panchenko,biemann}@informatik.uni-hamburg.de Abstract In this paper, we present Watasense, an unsupervised system for word sense disambiguation. Given a sentence, the system chooses the most relevant sense of each input word with respect to the semantic similarity between the given sentence and the synset constituting the sense of the target word. Watasense has two modes of operation. The sparse mode uses the traditional vector space model to estimate the most similar word sense corresponding to its context. The dense mode, instead, uses synset embeddings to cope with the sparsity problem. We describe the architecture of the present system and also conduct its evaluation on three different lexical semantic resources for Russian. We found that the dense mode substantially outperforms the sparse one on all datasets according to the adjusted Rand index. Keywords: word sense disambiguation, system, synset induction 1. Introduction Word sense disambiguation (WSD) is a natural language processing task of identifying the particular word senses of polysemous words used in a sentence. Recently, a lot of attention was paid to the problem of WSD for the Russian language (Lopukhin and Lopukhina, 2016; Lopukhin et al., 2017; Ustalov et al., 2017). This problem is especially dif- ficult because of both linguistic issues – namely, the rich morphology of Russian and other Slavic languages in gen- eral – and technical challenges like the lack of software and language resources required for addressing the problem. To address these issues, we present Watasense, an unsuper- vised system for word sense disambiguation. We describe its architecture and conduct an evaluation on three datasets for Russian. The choice of an unsupervised system is mo- tivated by the absence of resources that would enable a su- pervised system for under-resourced languages. Watasense is not strictly tied to the Russian language and can be ap- plied to any language for which a tokenizer, part-of-speech tagger, lemmatizer, and a sense inventory are available. The rest of the paper is organized as follows. Section 2 reviews related work. Section 3 presents the Watasense word sense disambiguation system, presents its architec- ture, and describes the unsupervised word sense disam- biguation methods bundled with it. Section 4 evaluates the system on a gold standard for Russian. Section 5 concludes with final remarks. 2. Related Work Although the problem of WSD has been addressed in many SemEval campaigns (Navigli et al., 2007; Agirre et al., 2010; Manandhar et al., 2010, inter alia), we focus here on word sense disambiguation systems rather than on the research methodologies. Among the freely available systems, IMS (“It Makes Sense”) is a supervised WSD system designed initially for the English language (Zhong and Ng, 2010). The system uses a support vector machine classifier to infer the par- ticular sense …

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