Abstract
Generally, lexical simplification replaces complex words in a sentence with simplified and synonymous words. Most current methods improve lexical simplification by optimizing ranking algorithm and their performance are limited. This paper utilizes a hybrid model through merging candidate words generated by a Context2vec neural model and a Context-aware model based on a weighted average method. The model consists of four steps: candidate word generation, candidate word selection, candidate word ranking, and candidate word merging. Through the evaluation on standard datasets, our hybrid model outperforms a list of baseline methods including Context2vec method, Context-aware method, and the state-of-the-art semantic-context ranking method, indicating its effectiveness in community-oriented lexical simplification task.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 61772146) and Natural Science Foundation of Guangdong Province (2018A030310051).
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Song, J., Shen, Y., Lee, J., Hao, T. (2020). A Hybrid Model for Community-Oriented Lexical Simplification. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_11
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