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Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation ...
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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. ... : In Proceedings of the the Conference on Empirical Methods on Natural Language Processing (EMNLP 2017). 2017. Copenhagen, Denmark. Association for Computational Linguistics ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; I.2.6; I.5.3; I.2.4
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URL: https://arxiv.org/abs/1707.06878
https://dx.doi.org/10.48550/arxiv.1707.06878
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Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl ...
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Unsupervised does not mean uninterpretable : the case for word sense induction and disambiguation
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Unsupervised, knowledge-free, and interpretable word sense disambiguation
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