Abstract
Reasonable evaluation of lexical complexity is the premise of multiple downstream NLP tasks such as text simplification. At present, there lacks of reliable Chinese lexical complexity datasets, while most of the existing foreign datasets only focus on the words that cause reading difficulty. This paper constructs a RCWI-Dataset for native Chinese speakers, which contains 40613 examples and three complexity categories. Each example is annotated by at least three annotators. We adopt comparison method to annotate words that are more difficult than average lexical complexity in sentences, so that we can get more information about word complexity and improve the reliability of our dataset. We provide baseline experiments based on feature engineering, the results show the validity of RCWI-Dataset.
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Acknowledgements
This work is funded by the Humanity and Social Science Youth foundation of Ministry of Education (19YJCZH230) and the Fundamental Research Funds for the Central Universities in BLCU (No. 17PT05).
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Que, M., Zhang, Y., Yu, D. (2021). RCWI: A Dataset for Chinese Complex Word Identification. In: Qin, B., Jin, Z., Wang, H., Pan, J., Liu, Y., An, B. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction. CCKS 2021. Communications in Computer and Information Science, vol 1466. Springer, Singapore. https://doi.org/10.1007/978-981-16-6471-7_25
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DOI: https://doi.org/10.1007/978-981-16-6471-7_25
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