@inproceedings{panchenko-etal-2017-unsupervised,
title = "Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation",
author = "Panchenko, Alexander and
Marten, Fide and
Ruppert, Eugen and
Faralli, Stefano and
Ustalov, Dmitry and
Ponzetto, Simone Paolo and
Biemann, Chris",
editor = "Specia, Lucia and
Post, Matt and
Paul, Michael",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-2016",
doi = "10.18653/v1/D17-2016",
pages = "91--96",
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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<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.</abstract>
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%0 Conference Proceedings
%T Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation
%A Panchenko, Alexander
%A Marten, Fide
%A Ruppert, Eugen
%A Faralli, Stefano
%A Ustalov, Dmitry
%A Ponzetto, Simone Paolo
%A Biemann, Chris
%Y Specia, Lucia
%Y Post, Matt
%Y Paul, Michael
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F panchenko-etal-2017-unsupervised
%X 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.
%R 10.18653/v1/D17-2016
%U https://aclanthology.org/D17-2016
%U https://doi.org/10.18653/v1/D17-2016
%P 91-96
Markdown (Informal)
[Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation](https://aclanthology.org/D17-2016) (Panchenko et al., EMNLP 2017)
ACL
- Alexander Panchenko, Fide Marten, Eugen Ruppert, Stefano Faralli, Dmitry Ustalov, Simone Paolo Ponzetto, and Chris Biemann. 2017. Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 91–96, Copenhagen, Denmark. Association for Computational Linguistics.