Abstract
Nowadays, research on stylistic features (SF) mainly focuses on two aspects: lexical elements and syntactic structures. The lexical elements act as the content of a sentence and the syntactic structures constitute the framework of a sentence. How to combine both aspects and exploit their common advantages is a challenging issue. In this paper, we propose a Principal Stylistic Features Analysis method (PSFA) to combine these two parts, and then mine the relations between features. From a statistical analysis point of view, many interesting linguistic phenomena can be found. Through the PSFA method, we finally extract some representative features which cover different aspects of styles. To verify the performance of these selected features, classification experiments are conducted. The results show that the elements selected by the PSFA method provide a significantly higher classification accuracy than other advanced methods.
This work is supported by Beijing Social Science Fund (16YYB021) and Project of Humanities and Social Sciences of Ministry of Education in China (17YJAZH056).
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Wu, H., Liu, Y. (2018). Syntax Enhanced Research Method of Stylistic Features. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_2
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