# Syntactic-GCN Bert based Chinese Event Extraction

arXiv: https://arxiv.org/abs/2112.09939 · HTML: https://mlsanity.com/paper/2112.09939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 18 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction](https://mlsanity.com/paper/2010.03009/) (2020)
- [Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction](https://mlsanity.com/paper/2004.05438/) (2020)
- [CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction](https://mlsanity.com/paper/2107.01583/) (2021)
- [ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19](https://mlsanity.com/paper/2105.01819/) (2021)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [Visualization Techniques to Enhance Automated Event Extraction](https://mlsanity.com/paper/2106.06588/) (2021)
- [Forecasting Crude Oil Price Using Event Extraction](https://mlsanity.com/paper/2111.09111/) (2021)
- [American Hate Crime Trends Prediction with Event Extraction](https://mlsanity.com/paper/2111.04951/) (2021)
- [Neural News Recommendation with Event Extraction](https://mlsanity.com/paper/2111.05068/) (2021)

## Abstract (arXiv)

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.