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http://elar.urfu.ru/handle/10995/89994
Название: | Unsupervised, knowledge-free, and interpretable word sense disambiguation |
Авторы: | Panchenko, A. Marten, F. Ruppert, E. Faralli, S. Ustalov, D. Ponzetto, S. P. Biemann, C. |
Дата публикации: | 2017 |
Издатель: | Association for Computational Linguistics (ACL) |
Библиографическое описание: | Unsupervised, knowledge-free, and interpretable word sense disambiguation / A. Panchenko, F. Marten, E. Ruppert, S. Faralli, et al. . — DOI 10.18653/v1/d17-2016 // EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. — 2017. — P. 91-96. |
Аннотация: | 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. © 2017 Association for Computational Linguistics. |
Ключевые слова: | KNOWLEDGE BASED SYSTEMS HUMAN-READABLE INTERPRETABILITY PREDICTIVE MODELING SEAMLESS INTEGRATION STATE OF THE ART WEB INTERFACE WORD SENSE WORD-SENSE DISAMBIGUATION NATURAL LANGUAGE PROCESSING SYSTEMS |
URI: | http://elar.urfu.ru/handle/10995/89994 |
Условия доступа: | info:eu-repo/semantics/openAccess cc-by |
Идентификатор SCOPUS: | 85072911197 |
Идентификатор PURE: | 11097509 |
ISBN: | 9781945626975 |
DOI: | 10.18653/v1/d17-2016 |
Сведения о поддержке: | Deutsche Forschungsgemeinschaft, DFG Russian Foundation for Basic Research, RFBR: 16-37-00354 Amazon Web Services, AWS Microsoft We acknowledge the support of the DFG under the “JOIN-T” project, the RFBR under project no. 16-37-00354 mol a, Amazon via the “AWS Research Grants” and Microsoft via the “Azure for Research” programs. Finally, we also thank four anonymous reviewers for their helpful comments. |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
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10.18653-v1-d17-2016.pdf | 864,76 kB | Adobe PDF | Просмотреть/Открыть |
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