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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 Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. ... NAACL 2021 2021; Rinaldi, Fabio; Shaitarova, Anastassia. - : Underline Science Inc., 2021 BASE Show details 2 Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish Shaitarova, Anastassia; Rinaldi, Fabio In: Shaitarova, Anastassia; Rinaldi, Fabio (2021). Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, Online, 6 June 2021 - 11 June 2021, ACL Anthology. (2021) Abstract: Negation is a linguistic universal that poses difficulties for cognitive and computational processing. Despite many advances in text analytics, negation resolution remains an acute and continuously researched question in Natural Language Processing. Reliable negation parsing affects results in biomedical text mining, sentiment analysis, machine translation, and many other fields. The availability of multilingual pre-trained general representation models makes it possible to experiment with negation detection in languages that lack annotated data. In this work we test the performance of two state-of-the-art contextual representation models, Multilingual BERT and XLM-RoBERTa. We resolve negation scope by conducting zero-shot transfer between English, Spanish, French, and Russian. Our best result amounts to a token-level F1-score of 86.86% between Spanish and Russian. We correlate these results with a linguistic negation typology and lexical capacity of the models. Keyword: 000 Computer science; 410 Linguistics; Institute of Computational Linguistics; knowledge & systems URL: https://doi.org/10.18653/v1/2021.naacl-srw.3 https://www.zora.uzh.ch/id/eprint/205787/ https://doi.org/10.5167/uzh-205787 https://www.zora.uzh.ch/id/eprint/205787/1/2021.naacl-srw.3.pdf https://aclanthology.org/2021.naacl-srw.3/ BASE Hide details 3 Modelling large parallel corpora. The Zurich Parallel Corpus Collection Graën, Johannes [Verfasser]; Kew, Tannon [Verfasser]; Shaitarova, Anastassia [Verfasser]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2019 DNB Subject Category Language Show details 4 Modelling large parallel corpora. The Zurich Parallel Corpus Collection ... Graën, Johannes; Kew, Tannon; Shaitarova, Anastassia. - : Leibniz-Institut für Deutsche Sprache, 2019 BASE Show details 5 Modelling Large Parallel Corpora: The Zurich Parallel Corpus Collection Graën, Johannes; Kew, Tannon; Shaitarova, Anastassia... In: Graën, Johannes; Kew, Tannon; Shaitarova, Anastassia; Volk, Martin (2019). Modelling Large Parallel Corpora: The Zurich Parallel Corpus Collection. In: Challenges in the Management of Large Corpora (CMLC-7), Cardiff, Wales, 22 July 2019 - 22 July 2019. (2019) BASE Show details 6 Modelling large parallel corpora. The Zurich Parallel Corpus Collection [Online resource] Graën, Johannes; Kew, Tannon; Shaitarova, Anastassia. IDS-Repository Show details

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