An unsupervised word sense disambiguation system for under-resourced languages

Ustalov, Dmitry ; Teslenko, Denis ; Panchenko, Alexander ; Chernoskutov, Mikhail ; Biemann, Chris ; Ponzetto, Simone Paolo

URL:	http://www.lrec-conf.org/proceedings/lrec2018/summ...
Additional URL:	https://arxiv.org/abs/1804.10686
Document Type:	Conference or workshop publication
Year of publication:	2018
Book title:	LREC 2018, 11th International Conference on Language Resources and Evaluation : 7-12 May 2018, Miyazaki (Japan)
Page range:	1018-1022
Conference title:	International Conference on Language Resources and Evaluation 2018
Location of the conference venue:	Miyazaki, Japan
Date of the conference:	May 7-12, 2018
Publisher:	Calzolari, Nicoletta
Place of publication:	Paris
Publishing house:	European Language Resources Association, ELRA-ELDA
ISBN:	979-10-95546-00-9
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Subject:	004 Computer science, internet
Keywords (English):	word sense disambiguation , unsupervised learning , evaluation
Abstract:	In this paper, we present Mnogoznal, 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. Mnogoznal 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.