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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter Biemann, Chris; Ruppert, Eugen; Wiedemann, Gregor... In: http://hw.oeaw.ac.at/8435-5 (2018) BASE Show details 2 Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation ... Panchenko, Alexander; Marten, Fide; Ruppert, Eugen; Faralli, Stefano; Ustalov, Dmitry; Ponzetto, Simone Paolo; Biemann, Chris. - : arXiv, 2017 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 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.6; I.5.3; I.2.4 URL: https://arxiv.org/abs/1707.06878 https://dx.doi.org/10.48550/arxiv.1707.06878 BASE Hide details 3 Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl ... Panchenko, Alexander; Ruppert, Eugen; Faralli, Stefano. - : arXiv, 2017 BASE Show details 4 Unsupervised does not mean uninterpretable : the case for word sense induction and disambiguation Biemann, Chris; Ponzetto, Simone Paolo; Ruppert, Eugen. - : Association for Computational Linguistics, 2017 BASE Show details 5 Unsupervised, knowledge-free, and interpretable word sense disambiguation Faralli, Stefano; Panchenko, Alexander; Marten, Fide. - : Association for Computational Linguistics, 2017 BASE Show details 6 TAXI at SemEval-2016 Task 13: a taxonomy induction method based on lexico-syntactic patterns, substrings and focused crawling Fairon, Cédrick; Faralli, Stefano; Panchenko, Alexander. - : Association for Computational Linguistics, 2016 BASE Show details

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