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Erschienen in:
2023 | OriginalPaper | Buchkapitel
An Improved Method for Chinese Relationship Extraction
verfasst von : Zhuo Su, Bo Liu, Jianqiang Li, Yan Pei
Erschienen in: Frontier Computing
Verlag: Springer Nature Singapore
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Abstract
In the field of information extraction, Chinese relationship extraction has become a problem which deserves the attention in both academia and industry. Unlike English, the semantic relations in Chinese are more complex with ambiguity. In order to perform more accurate Chinese relationship extraction, this paper discards the traditional pipeline-level extraction method and adopts a joint-level extraction method, proposing an RSO model. On the whole, the RSO model is divided into an encoding module, a subject entity extraction module and an object entity extraction module, which solve some problems in Chinese relationship extraction. The encoding module incorporates the four layers of the RoBERTa model that work best for textual tasks. The subject entity extraction module and the object entity extraction module use the concept of pointer annotation. Finally, the performance of RSO is compared with related work on the Chinese relationship extraction dataset, proving the effectiveness and feasibility of the proposed model.