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

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

Published on: September 5, 2019

A multi-task information extraction Chinese dataset for APT cyber threat intelligence.

Lu Sun¹, Ga Xiang^2,3, Zicheng Tan¹

¹College of Computer Science, Beijing Information Science and Technology University, Beijing, 102206, China.

Scientific Data

|May 24, 2026

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This study introduces a new dataset for Chinese Advanced Persistent Threats (APTs) cyber threat intelligence. It aids in extracting crucial information for better detection of complex cyber attacks.

Area of Science:

Cybersecurity
Natural Language Processing
Data Science

Background:

Advanced Persistent Threats (APTs) pose significant risks due to their complex attack chains.
Effective detection of APTs relies on extracting critical knowledge from Cyber Threat Intelligence (CTI).
A scarcity of high-quality, multi-task information extraction datasets for Chinese APT CTI hinders model development.

Purpose of the Study:

To address the lack of comprehensive datasets for Chinese APT CTI.
To propose a novel multi-task information extraction dataset for Chinese APT scenarios.
To facilitate improved training and performance of APT detection models.

Main Methods:

Developed a multi-task dataset from 116 CTI reports, adhering to the STIX 2.1 standard.

Related Experiment Videos

Last Updated: Jun 21, 2026

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

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

Published on: September 5, 2019

The dataset covers entity, relation, and event extraction tasks.

Annotated 2,574 entities, 1,506 relations, and 139 event instances across 808 sentences.

Main Results:

The proposed dataset offers superior task coverage, annotation granularity, and structural hierarchy compared to existing APT CTI datasets.
Validated the dataset's utility using several baseline information extraction models.
Demonstrated the dataset's effectiveness in supporting APT intelligence modeling.

Conclusions:

The newly created dataset significantly enhances capabilities for Chinese APT CTI analysis.
This resource is crucial for advancing cybersecurity research and improving APT detection models.
The dataset provides a robust foundation for future work in automated CTI processing.