Unsupervised, knowledge-free, and interpretable word sense disambiguation

Panchenko, Alexander ; Marten, Fide ; Ruppert, Eugen ; Faralli, Stefano ; Ustalov, Dmitry ; Ponzetto, Simone Paolo ; Biemann, Chris

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URL: https://madoc.bib.uni-mannheim.de/43326
Additional URL: http://aclweb.org/anthology/D17-2
URN: urn:nbn:de:bsz:180-madoc-433264
Document Type: Conference or workshop publication
Year of publication: 2017
Book title: The Conference on Empirical Methods in Natural Language Processing - proceedings of System Demonstrations : September 9-11, 2017, Copenhagen, Denmark : EMNLP 2017
Page range: 91-96
Conference title: 2017 Conference on Empirical Methods in Natural Language Processing
Location of the conference venue: Copenhagen, Denmark
Date of the conference: September 7-11, 2017
Publisher: Specia, Lucia
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
ISBN: 978-1-945626-97-5
Publication language: English
Institution: School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Subject: 004 Computer science, internet
Abstract: Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images. We present a WSD system that bridges the gap between these two so far disconnected groups of methods. Namely, our system, providing access to several state-of-the-art WSD models, aims to be interpretable as a knowledge-based system while it remains completely unsupervised and knowledge-free. The presented tool features a Web interface for all-word disambiguation of texts that makes the sense predictions human readable by providing interpretable word sense inventories, sense representations, and disambiguation results. We provide a public API, enabling seamless integration.

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