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Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence

5th China Conference, CCKS 2020, Nanchang, China, November 12–15, 2020, Revised Selected Papers

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Huajun Chen

    1. Zhejiang University, Hangzhou, China

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  2. Kang Liu

    1. Chinese Academy of Sciences, Institute of Automation, Beijing, China

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  3. Yizhou Sun

    1. University of California, Los Angeles, USA

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  4. Suge Wang

    1. Shanxi University, Taiyuan, China

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  5. Lei Hou

    1. Tsinghua University, Beijing, China

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Part of the book series: Communications in Computer and Information Science (CCIS, volume 1356)

Included in the following conference series:

Conference proceedings info: CCKS 2020.

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Table of contents (26 papers)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. Knowledge Extraction: Lexical and Entity

    1. Front Matter

      Pages 1-1
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    2. Chinese Punctuation Prediction with Adaptive Attention and Dependency Tree

      • Zelong Yan, Jianzong Wang, Ning Cheng, Tianbo Wu, Jing Xiao
      Pages 3-14

    3. Sememe Tree Prediction for English-Chinese Word Pairs

      • Baoju Liu, Xuejun Shang, Liqing Liu, Yuanpeng Tan, Lei Hou, Juanzi Li
      Pages 15-27

    4. FGN: Fusion Glyph Network for Chinese Named Entity Recognition

      • Zhenyu Xuan, Rui Bao, Shengyi Jiang
      Pages 28-40

    5. Semantic Label Enhanced Named Entity Recognition with Incompletely Annotated Data

      • Yunke Zhang, Long Yu, Zhanfeng Liu
      Pages 41-53

    6. Named Entity Recognition Using a Semi-supervised Model Based on BERT and Bootstrapping

      • Yuanyuan Liu, Xiang Li, Jiaxin Shi, Lei Zhang, Juanzi Li
      Pages 54-63

    7. Reinforcement Learning for Clue Selection in Web-Based Entity Translation Mining

      • Lingyong Yan, Xianpei Han, Le Sun
      Pages 64-77

    8. Neural Fusion Model for Chinese Semantic Matching

      • Renshou Wu, Qingliang Miao, Miaoyuan Shi, Min Chu, Kai Yu
      Pages 78-90

  3. Knowledge Extraction: Relation

    1. Front Matter

      Pages 91-91
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    2. Improving Relation Extraction Using Semantic Role and Multi-task Learning

      • Zhandong Zhu, Jindian Su, Xiaobin Hong
      Pages 93-105

    3. Learning Global Representations for Document-Level Biomedical Relation Extraction

      • Lishuang Li, Hongbin Lu, Shuang Qian, Shiyi Zhao, Yifan Zhu
      Pages 106-118

    4. Type-Aware Open Information Extraction via Graph Augmentation Model

      • Qinghua Wen, Yunzhe Tian, Xiaohui Zhang, Ruoyun Hu, Jinsong Wang, Lei Hou et al.
      Pages 119-131

    5. IBRE: An Incremental Bootstrapping Approach for Chinese Appointment and Dismissal Relation Extraction

      • Lu Mao, Meiling Wang, Changliang Li, Junfeng Fan, Changyu Hou
      Pages 132-144

  4. Knowledge Extraction: Event

    1. Front Matter

      Pages 145-145
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    2. Hybrid Syntactic Graph Convolutional Networks for Chinese Event Detection

      • Xiaobo Ma, Yongbin Liu, Chunping Ouyang
      Pages 147-159

    3. Meta Learning for Event Argument Extraction via Domain-Specific Information Enhanced

      • Hang Yang, Yubo Chen, Kang Liu, Jun Zhao
      Pages 160-172

    4. A Survey on Event Relation Identification

      • Ya Liu, Jiachen Tian, Lan Zhang, Yibo Feng, Hong Fang
      Pages 173-184

    5. Employing Multi-cues to Identify Event Factuality

      • Liumin Zhang, Peifeng Li, Zhong Qian, Xiaoxu Zhu, Qiaoming Zhu
      Pages 185-195

    6. End-to-End Event Factuality Identification via Hybrid Neural Networks

      • Jinjuan Cao, Zhong Qian, Peifeng Li, Xiaoxu Zhu, Qiaoming Zhu
      Pages 196-208
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  1. Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence

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About this book

This book constitutes the refereed proceedings of the 5th China Conference on Knowledge Graph and Semantic Computing, CCKS 2020, held in Nanchang, China, in November 2020. 
The 26 revised full papers presented were carefully reviewed and selected from 173 submissions. The papers are organized in topical sections on ​knowledge extraction: lexical and entity; knowledge extraction: relation; knowledge extraction: event; knowledge applications: question answering, dialogue, decision support, and recommendation.

Editors and Affiliations

  • Zhejiang University, Hangzhou, China

    Huajun Chen

  • Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Kang Liu

  • University of California, Los Angeles, USA

    Yizhou Sun

  • Shanxi University, Taiyuan, China

    Suge Wang

  • Tsinghua University, Beijing, China

    Lei Hou

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