Syntactic-GCN Bert based Chinese Event Extraction
This work addresses the challenge of extracting structured events from Chinese text, which lags behind English due to linguistic complexities, offering a domain-specific solution for processing social media and news data.
The paper tackled Chinese event extraction by proposing an integrated neural framework that combines BERT for semantic features with POS embeddings and GCN for syntactic features, achieving significant performance improvements over benchmarks on a real-world dataset.
With the rapid development of information technology, online platforms (e.g., news portals and social media) generate enormous web information every moment. Therefore, it is crucial to extract structured representations of events from social streams. Generally, existing event extraction research utilizes pattern matching, machine learning, or deep learning methods to perform event extraction tasks. However, the performance of Chinese event extraction is not as good as English due to the unique characteristics of the Chinese language. In this paper, we propose an integrated framework to perform Chinese event extraction. The proposed approach is a multiple channel input neural framework that integrates semantic features and syntactic features. The semantic features are captured by BERT architecture. The Part of Speech (POS) features and Dependency Parsing (DP) features are captured by profiling embeddings and Graph Convolutional Network (GCN), respectively. We also evaluate our model on a real-world dataset. Experimental results show that the proposed method outperforms the benchmark approaches significantly.