Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts

被引:15
|
作者
Tao, Qiongxing [2 ]
Luo, Xiangfeng [1 ,2 ]
Wang, Hao [1 ,2 ]
Xu, Richard [3 ]
机构
[1] Shanghai Inst Adv Commun & Data Sci, Shanghai, Peoples R China
[2] Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China
[3] Univ Technol Sydney, Sch Elect & Data Engn, Ultimo, Australia
来源
2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019) | 2019年
基金
中国国家自然科学基金;
关键词
relation extraction; syntactic indicators; sentential context;
D O I
10.1109/ICTAI.2019.00227
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
State-of-the-art methods for relation extraction consider the sentential context by modeling the entire sentence. However, syntactic indicators, certain phrases or words like prepositions that are more informative than other words and may be beneficial for identifying semantic relations. Other approaches using fixed text triggers capture such information but ignore the lexical diversity. To leverage both syntactic indicators and sentential contexts, we propose an indicator-aware approach for relation extraction. Firstly, we extract syntactic indicators under the guidance of syntactic knowledge. Then we construct a neural network to incorporate both syntactic indicators and the entire sentences into better relation representations. By this way, the proposed model alleviates the impact of noisy information from entire sentences and breaks the limit of text triggers. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model significantly outperforms the state-of-the-art methods.
引用
收藏
页码:1574 / 1580
页数:7
相关论文
共 50 条
  • [31] Enhancing Continual Relation Extraction via Classifier Decomposition
    Xia, Heming
    Wang, Peiyi
    Liu, Tianyu
    Lin, Binghuai
    Cao, Yunbo
    Sui, Zhifang
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2023), 2023, : 10053 - 10062
  • [32] Freepal: A Large Collection of Deep Lexico-Syntactic Patterns for Relation Extraction
    Kirschnick, Johannes
    Akbik, Alan
    Hemsen, Holmer
    LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2014, : 2071 - 2075
  • [33] Extraction of Key Postures using Shape Contexts
    Lee, Geum-Boon
    Odoyo, Wilfred O.
    Yeom, Jeong-Nam
    Cho, Beom-Joon
    11TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY, VOLS I-III, PROCEEDINGS,: UBIQUITOUS ICT CONVERGENCE MAKES LIFE BETTER!, 2009, : 1311 - 1314
  • [34] Research on Chinese Medical Entity Relation Extraction Based on Syntactic Dependency Structure Information
    Zhang, Qinghui
    Wu, Meng
    Lv, Pengtao
    Zhang, Mengya
    Lv, Lei
    APPLIED SCIENCES-BASEL, 2022, 12 (19):
  • [35] Integrating syntactic and sequential information and semantic representation for chemical-disease relation extraction
    Wang, Qinghu
    Jiang, Mingyang
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 125 : 69 - 70
  • [36] Tree kernel-based semantic relation extraction with rich syntactic and semantic information
    Zhou Guodong
    Qian Longhua
    Fan Jianxi
    INFORMATION SCIENCES, 2010, 180 (08) : 1313 - 1325
  • [37] Extraction of Opinion Target Using Syntactic Rules in Urdu Text
    Rana, Toqir A.
    Bakht, Bahrooz
    Afzal, Mehtab
    Mian, Natash Ali
    Iqbal, Muhammad Waseem
    Khalid, Abbas
    Naqvi, Muhammad Raza
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2021, 29 (03): : 839 - 853
  • [38] Research on Dialogue Entity Relation Extraction with Enhancing Character Information
    Xu Y.
    Jiang Y.
    Zhang Y.
    He W.
    Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58 (01): : 13 - 20
  • [39] Enhancing Multimodal Entity and Relation Extraction With Variational Information Bottleneck
    Cui, Shiyao
    Cao, Jiangxia
    Cong, Xin
    Sheng, Jiawei
    Li, Quangang
    Liu, Tingwen
    Shi, Jinqiao
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2024, 32 : 1274 - 1285
  • [40] Enhancing Document-Level Relation Extraction with Entity Pronoun Resolution and Relation Correlation
    Pi, Qiankun
    Lu, Jicang
    Sun, Yepeng
    Zhu, Taojie
    Xia, Yi
    Yang, Chenguang
    NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, PT II, NLPCC 2024, 2025, 15360 : 174 - 186