Task-Collaboration Representation Enhanced Joint Extraction Model for Elements and Relationships

LIU Xiao-ming; WANG Hang; YANG Guan; LIU Jie; CAO Meng-yuan

doi:10.12263/DZXB.20230623

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Task-Collaboration Representation Enhanced Joint Extraction Model for Elements and Relationships

PAPERS | 更新时间：2025-12-11

- Task-Collaboration Representation Enhanced Joint Extraction Model for Elements and Relationships
- ACTA ELECTRONICA SINICA Vol. 52, Issue 6, Pages: 1955-1962(2024)
- 作者机构：
  
  1.中原工学院计算机学院,河南郑州 450007
  2.郑州市文本处理与图像理解重点实验室,河南郑州 450007
  3.北方工业大学信息学院,北京 100144
  4.国家语委中国语言智能研究中心,北京 100089
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2020AAA0109703);National Natural Science Foundation of China(62076167;61772020;U23B2029);Key Scientific Research Project of Higher Education Institutions in Henan Province(23A520022)
- DOI：10.12263/DZXB.20230623
  CLC： TP391
- Received：03 July 2023，
  
  Revised：2024-01-26，
  
  Published：25 June 2024
- 稿件说明：
移动端阅览
刘小明, 王杭, 杨关, 等. 任务协作表示增强的要素及关系联合抽取模型[J]. 电子学报, 2024, 52(06): 1955-1962.

LIU Xiao-ming, WANG Hang, YANG Guan, et al. Task-Collaboration Representation Enhanced Joint Extraction Model for Elements and Relationships[J]. Acta Electronica Sinica, 2024, 52(06): 1955-1962.
刘小明, 王杭, 杨关, 等. 任务协作表示增强的要素及关系联合抽取模型[J]. 电子学报, 2024, 52(06): 1955-1962. DOI：10.12263/DZXB.20230623

LIU Xiao-ming, WANG Hang, YANG Guan, et al. Task-Collaboration Representation Enhanced Joint Extraction Model for Elements and Relationships[J]. Acta Electronica Sinica, 2024, 52(06): 1955-1962. DOI：10.12263/DZXB.20230623

摘要

对文本中诸如实体与关系、事件及其论元等要素及其特定关系的联合抽取是自然语言处理的一项关键任务.现有研究大多采用统一编码或参数共享的方式隐性处理任务间的交互，缺乏对任务之间特定关系的显式建模，从而限制模型充分利用任务间的关联信息并影响任务间的有效协同.为此，提出了一种基于任务协作表示增强的要素及关系联合抽取模型（Task-Collaboration Representation Enhanced model for joint extraction of elements and relationships，TCRE）.该模型旨在从多个阶段处理任务间的特定关系，帮助子任务进行更细致的调节和优化，促进整体性能的提升.在三个关系抽取和一个事件抽取数据集上进行实验，TCRE在实体识别和关系提取任务上平均性能分别提高0.57%和0.77%，在触发词识别和论元角色分类任务上分别提高0.7%和1.4%.此外，TCRE还显示出在缓解“跷跷板现象”方面的作用.

Abstract

Jointly extracting elements like entities and their relationships

as well as events and their arguments

is a crucial natural language processing task. Current methods

primarily based on unified coding or parameter sharing

fail to explicitly model inter-task relationships. This limitation restricts the use of inter-task correlations and hinders effective collaboration. To address this

we propose a task-collaboration representation enhanced model for joint extraction of elements and relationships (TCRE). TCRE strategically captures and leverages specific inter-task relationship representations across multiple stages

facilitating precise tuning and optimization of subtasks

thereby enhancing overall model performance. In evaluations on three relation extraction and one event extraction datasets

TCRE demonstrated performance improvements of 0.57% in entity recognition

0.77% in relation extraction

0.7% in trigger word recognition

and 1.4% in argument role classification. Additionally

TCRE effectively mitigates the “seesaw phenomenon”.

关键词

Keywords

references

隗昊 , 唐焕玲 , 周爱 , 等 . 基于双路分段注意力神经张量网络的临床文本关系抽取 [J ] . 电子学报 , 2023 , 51 ( 3 ): 658 - 665 .

WEI H , TANG H L , ZHOU A , et al . Clinical relation extraction via dual piecewise attention neural tensor network‍ [J ] . Acta Electronica Sinica , 2023 , 51 ( 3 ): 658 - 665 . (in Chinese)

LU Y J , LIU Q , DAI D , et al . Unified structure generation for universal information extraction [C ] // Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Stroudsburg : Association for Computational Linguistics , 2022 : 5755 - 5772 .

FU T J , LI P H , MA W Y . GraphRel: Modeling text as relational graphs for joint entity and relation extraction [C ] // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Stroudsburg : Association for Computational Linguistics , 2019 : 1409 - 1418 .

REN F L , ZHANG L H , YIN S J , et al . A novel global feature-oriented relational triple extraction model based on table filling [C ] // Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Stroudsburg : Association for Computational Linguistics , 2021 : 2646 - 2656 .

XU N , XIE H H , ZHAO D Y . A novel joint framework for multiple Chinese events extraction [C ] // China National Conference on Chinese Computational Linguistics . Cham : Springer , 2020 : 174 - 183 .

冯兴杰 , 赵新阳 , 冯小荣 . 基于软参数共享的事件联合抽取方法 [J ] . 计算机应用研究 , 2023 , 40 ( 1 ): 91 - 96 .

FENG X J , ZHAO X Y , FENG X R . Joint event extraction method based on soft parameter sharing [J ] . Application Research of Computers , 2023 , 40 ( 1 ): 91 - 96 . (in Chinese)

ZHONG Z X , CHEN D Q . A frustratingly easy approach for entity and relation extraction [C ] // Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Stroudsburg : Association for Computational Linguistics , 2021 : 50 - 61 .

YAN Z H , ZHANG C , FU J L , et al . A partition filter network for joint entity and relation extraction [C ] // Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Stroudsburg : Association for Computational Linguistics , 2021 : 185 - 197 .

TANG H Y , LIU J N , ZHAO M , et al . Progressive layered extraction (PLE): A novel multi-task learning (MTL) model for personalized recommendations [C ] // Proceedings of the 14th ACM Conference on Recommender Systems . New York : ACM , 2020 : 269 - 278 .

WEI X , CUI X Y , CHENG N , et al . Zero-shot information extraction via chatting with ChatGPT [EB/OL ] . [2023 ] . http://arxiv.org/abs/2302.10205 http://arxiv.org/abs/2302.10205 .

LOU J , LU Y J , DAI D , et al . Universal information extraction as unified semantic matching [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2023 , 37 ( 11 ): 13318 - 13326 .

XIONG X , LIU Y , LIUA , et al . A multi-gate encoder for joint entity and relation extraction [C ] // Proceedings of the 21st Chinese National Conference on Computational Linguistics . Nanchang : Chinese Information Processing Society of China , 2022 : 848 - 860 .

LIU X D , HE P C , CHEN W Z , et al . Multi-task deep neural networks for natural language understanding [C ] // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Stroudsburg : Association for Computational Linguistics , 2019 : 4487 - 4496 .

MOU L L , MEN R , LI G , et al . Natural language inference by tree-based convolution and heuristic matching‍ [C ] // Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Stroudsburg : Association for Computational Linguistics , 2016 : 130 - 136 .

CHRISTOPHER WALKER . ACE 2005 multilingual training corpus [EB/OL ] . [2023 ] . https://doi.org/10.35111/mwxc-vh88 https://doi.org/10.35111/mwxc-vh88 .

DODDINGTON G , MITCHELL A , PRZYBOCKI M , et al . The automatic content extraction (ACE) program - Tasks, data, and evaluation [C ] // Proceedings of the Fourth International Conference on Language Resources and Evaluation . Lisbon : European Language Resources Association (ELRA) , 2004 : 837 - 840 .

LUAN Y , HE L H , OSTENDORF M , et al . Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction [C ] // Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Stroudsburg : Association for Computational Linguistics , 2018 : 3219 - 3232 .

LAN Z Z , CHEN M D , GOODMAN S , et al . ALBERT: A lite BERT for self-supervised learning of language representations [EB/OL ] . [2023 ] . http://arxiv.org/abs/1909.11942 http://arxiv.org/abs/1909.11942 .

BELTAGY I , LO K , COHAN A . SciBERT: A pretrained language model for scientific text [C ] // Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Stroudsburg : Association for Computational Linguistics , 2019 : 3615 - 3620 .

LIU Y H , OTT M , GOYAL N , et al . RoBERTa: A robustly optimized BERT pretraining approach [EB/OL ] . [2023 ] . http://arxiv.org/abs/1907.11692 http://arxiv.org/abs/1907.11692 .

WANG S H , SUN X F , LI X Y , et al . GPT-NER: Named entity recognition via large language models [EB/OL ] . [2023 ] . http://arxiv.org/abs/2304.10428 http://arxiv.org/abs/2304.10428 .

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