Study on Chinese Discourse Semantic Annotation Based on Semantic Dependency Graph

被引:0
|
作者
Chen, Bo [1 ]
Lyu, Chen [1 ]
Ji, Ziqing [2 ]
机构
[1] Guangdong Univ Foreign Studies, Collaborat Innovat Ctr Language Res & Serv, Guangzhou 510420, Peoples R China
[2] Univ Maryland, Coll Comp Sci, College Pk, MD 20742 USA
来源
基金
中国国家自然科学基金; 国家教育部科学基金资助;
关键词
Semantic dependency graph; Discourse; Semantic annotation; Recursion;
D O I
10.1007/978-3-030-04015-4_64
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Semantic annotation of discourse is always one of most important tasks in natural language processing (NLP). We proposed a complex semantic representation mechanism for Chinese complex sentences. The semantic relations between sentences and between words can be represented as a semantic dependency graph by recursive directed graph. We studied the basic definition, the types of relations, and dependency direction of semantic dependence, and discussed the formal representation mechanism of semantic dependency from phrase-level, sentence-level and discourse-level. The semantic dependency graph model is characterized by allowing multiple correlations, allowing recursion and nesting, and formal representations are shown in recursive directed graph. The semantic dependency graph can more comprehensively represent the semantic relations between words and between the clauses in the discourse.
引用
收藏
页码:733 / 742
页数:10
相关论文
共 50 条
  • [21] Ontology-based Semantic Annotation in Semantic Query
    Wu, Chengwen
    Jin, Kezhong
    Huang, Changcheng
    Liu, Wenbin
    [J]. ACC 2009: ETP/IITA WORLD CONGRESS IN APPLIED COMPUTING, COMPUTER SCIENCE, AND COMPUTER ENGINEERING, 2009, : 280 - 283
  • [22] Automatically Semantic Annotation of Network Document Based on Domain Knowledge Graph
    Wu, Yuezhong
    Wang, Zhihong
    Chen, Shuhong
    Wang, Guojun
    Li, Changyun
    [J]. 2017 15TH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS AND 2017 16TH IEEE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING AND COMMUNICATIONS (ISPA/IUCC 2017), 2017, : 715 - 721
  • [23] Graph Based Automatic Protein Function Annotation Improved by Semantic Similarity
    Sarker, Bishnu
    Khare, Navya
    Devignes, Marie-Dominique
    Aridhi, Sabeur
    [J]. BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2020), 2020, 12108 : 261 - 272
  • [24] Chinese Sentence Correlation Analyzing based on Semantic Dependency Method
    Zhu, Haiping
    Chen, Yan
    Yang, Yang
    Ma, Qian
    [J]. 2011 INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATIONS AND CONTROL (ICECC), 2011, : 1932 - 1935
  • [25] A Neural Transition-Based Approach for Semantic Dependency Graph Parsing
    Wang, Yuxuan
    Che, Wanxiang
    Guo, Jiang
    Liu, Ting
    [J]. THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 5561 - 5568
  • [26] The development of a semantic annotation scheme for Chinese kinship
    Qian, Yufang
    Piao, Scott
    [J]. CORPORA, 2009, 4 (02) : 189 - 208
  • [27] The Analysis and Annotation of Semantic Modality for Chinese Words
    Zhang, Shen
    Jia, Jia
    Wang, Xiaohui
    Cai, Lianhong
    [J]. 11TH CHINESE LEXICAL SEMANTICS WORKSHOP (CKSW2010), 2010, : 143 - 150
  • [28] Semantic Augmentation in Chinese Adversarial Corpus for Discourse Relation Recognition Based on Internal Semantic Elements
    Hua, Zheng
    Yang, Ruixia
    Feng, Yanbin
    Yin, Xiaojun
    [J]. ELECTRONICS, 2024, 13 (10)
  • [29] Leverage External Knowledge and Self-attention for Chinese Semantic Dependency Graph Parsing
    Liu, Dianqing
    Zhang, Lanqiu
    Shao, Yanqiu
    Sun, Junzhao
    [J]. INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2021, 28 (02): : 447 - 458
  • [30] Semantic Annotation for Places in LBSN through Graph Embedding
    Wang, Yan
    Qin, Zongxu
    Pang, Jun
    Zhang, Yang
    Xin, Jin
    [J]. CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2017, : 2343 - 2346