Abstract
Lexical simplification under a given vocabulary scope for specified communities would potentially benefit many applications such as second language learning and cognitive disabilities education. This paper proposes a new concise ranking strategy for incorporating semantic and context for lexical simplification to a restricted scope. Our approach utilizes WordNet-based similarity calculation for semantic expansion and ranking. It then uses Part-of-Speech tagging and Google 1T 5-gram corpus for context-based ranking. Our experiments are based on a publicly available data sets. Through the comparison with baseline methods including Google Word2vec and four-step method, our approach achieves best F1 measure as 0.311 and Oot F1 measure as 0.522, respectively, demonstrating its effectiveness in combining semantic and context for English lexical simplification.
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Acknowledgments
This work was supported by the Innovation and Technology Fund (Ref: ITS/132/15) of the Innovation and Technology Commission, the Government of the Hong Kong Special Administrative Region, National Natural Science Foundation of China (No. 61772146 & No. 61403088), and Innovative School Project in Higher Education of Guangdong Province (No. YQ2015062).
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Hao, T., Xie, W., Lee, J. (2018). A Semantic-Context Ranking Approach for Community-Oriented English Lexical Simplification. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_68
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