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Spatial multi-arrangement for clustering and multi-way similarity dataset construction
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Majewska, Olga; McCarthy, D; van den Bosch, J. - : European Language Resources Association, 2020. : LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, 2020
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Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis
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Majewska, Olga; Vulic, Ivan; McCarthy, Diana. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.423, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
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Common sense or world knowledge? Investigating adapter-based knowledge injection into pretrained transformers
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XCOPA: A multilingual dataset for causal commonsense reasoning
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A neural classification method for supporting the creation of BioVerbNet ...
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A neural classification method for supporting the creation of BioVerbNet ...
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A neural classification method for supporting the creation of BioVerbNet ...
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A Neural Classification Method for Supporting the Creation of BioVerbNet ...
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A Neural Classification Method for Supporting the Creation of BioVerbNet
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A neural classification method for supporting the creation of BioVerbNet
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Acquiring verb classes through bottom-up semantic verb clustering ...
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Investigating the cross-lingual translatability of VerbNet-style classification. ...
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Investigating the cross-lingual translatability of VerbNet-style classification.
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Abstract:
VerbNet-the most extensive online verb lexicon currently available for English-has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper.
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URL: https://www.repository.cam.ac.uk/handle/1810/276130 https://doi.org/10.17863/CAM.23410
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Acquiring verb classes through bottom-up semantic verb clustering
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