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Erschienen in:
Radical Enhanced Chinese Word Embedding Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Syntax Enhanced Research Method of Stylistic Features

verfasst von : Haiyan Wu, Ying Liu

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Radical Enhanced Chinese Word Embedding
Nächstes Kapitel Addressing Domain Adaptation for Chinese Word Segmentation with Instances-Based Transfer Learning
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Metadaten
Titel
Syntax Enhanced Research Method of Stylistic Features
verfasst von
Haiyan Wu
Ying Liu
Copyright-Jahr
2018
Buch
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01716-3_2