Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/114024
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Title: Complex-ZH : a new dataset for lexical complexity prediction in Mandarin and Cantonese
Authors: Qiu, L 
Guo, S 
Wong, TS 
Chersoni, E 
Lee, J
Huang, CR 
Issue Date: 2024
Source: In Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024), p. 20–26. Miami, Florida, USA: Association for Computational Linguistics, 2024
Abstract: The prediction of lexical complexity in context is assuming an increasing relevance in Natural Language Processing research, since identifying complex words is often the first step of text simplification pipelines. To the best of our knowledge, though, datasets annotated with complex words are available only for English and for a limited number of Western languages.In our paper, we introduce CompLex-ZH, a dataset including words annotated with complexity scores in sentential contexts for Chinese. Our data include sentences in Mandarin and Cantonese, which were selected from a variety of sources and textual genres. We provide a first evaluation with baselines combining hand-crafted and language models-based features.
Publisher: Association for Computational Linguistics
ISBN: 979-8-89176-176-6
DOI: 10.18653/v1/2024.tsar-1.3
Description: Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024), Miami, Florida, USA, 15 November 2024
Rights: ©2024 Association for Computational Linguistics
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The following publication Le Qiu, Shanyue Guo, Tak-Sum Wong, Emmanuele Chersoni, John Lee, and Chu-Ren Huang. 2024. CompLex-ZH: A New Dataset for Lexical Complexity Prediction in Mandarin and Cantonese. In Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024), pages 20–26, Miami, Florida, USA. Association for Computational Linguistics is available at https://doi.org/10.18653/v1/2024.tsar-1.3.
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