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Related Experiment Video

Updated: Dec 14, 2025

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

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Published on: September 5, 2019

A transition-based neural framework for Chinese information extraction.

Wenzhi Huang^1,2, Junchi Zhang², Donghong Ji¹

¹Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, Hubei, China.

|July 16, 2020

Summary

This summary is machine-generated.

This study introduces a novel joint model for Chinese information extraction, integrating entity recognition, relation extraction, and event detection. The new approach improves accuracy by avoiding error propagation and leveraging cross-task dependencies, achieving state-of-the-art results.

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Last Updated: Dec 14, 2025

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Published on: September 5, 2019

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Area of Science:

Natural Language Processing
Artificial Intelligence
Computational Linguistics

Background:

Traditional Chinese information extraction uses a pipelined approach (word segmentation, entity recognition, relation extraction, event detection).
This sequential method leads to error propagation and fails to leverage cross-task dependencies.
Existing models struggle to integrate these interconnected tasks effectively.

Purpose of the Study:

To develop a unified model for joint Chinese information extraction.
To overcome limitations of pipelined approaches in information extraction.
To improve the accuracy and efficiency of entity, relation, and event extraction.

Main Methods:

A novel transition-based model is proposed for joint entity recognition, relation extraction, and event detection.
A hybrid lattice structure is incorporated to integrate subword-level information.
This approach eliminates the need for external word tokenizers.

Main Results:

The proposed joint model significantly outperforms previous methods on standard ACE benchmarks.
The hybrid lattice network effectively incorporates subword information.
The unified approach demonstrates superior performance in Chinese information extraction tasks.

Conclusions:

Jointly performing entity recognition, relation extraction, and event detection is more effective than pipelined methods.
The proposed transition-based model with a hybrid lattice structure achieves state-of-the-art results.
This work advances Chinese information extraction by addressing error propagation and enhancing task integration.