A Hybrid Model for Community-Oriented Lexical Simplification

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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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Song, J., Shen, Y., Lee, J., & Hao, T. (2020). A Hybrid Model for Community-Oriented Lexical Simplification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12430 LNAI, pp. 132–144). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60450-9_11

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