任务协作表示增强的要素及关系联合抽取模型

刘小明; 王杭; 杨关; 刘杰; 曹梦远

doi:10.12263/DZXB.20230623

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任务协作表示增强的要素及关系联合抽取模型

学术论文 | 更新时间：2025-12-11

- 任务协作表示增强的要素及关系联合抽取模型
- Task-Collaboration Representation Enhanced Joint Extraction Model for Elements and Relationships
- 电子学报 2024年52卷第6期页码：1955-1962
- 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：
  
  刘小明男，1979年6月出生于河南省许昌市.现为中原工学院计算机学院讲师、硕士生导师.主要研究方向为自然语言、中文信息处理和机器学习. E-mail: ming616@zut.edu.cn
  王杭男，1997年11月出生于河南省商丘市.现为中原工学院计算机学院硕士研究生，研究方向为自然语言处理. E-mail: 2021107253@zut.edu.cn
  杨关男，1974年6月出生于陕西省西安市.现为中原工学院计算机学院副教授、硕士生导师.研究领域为机器学习与图像处理.
  刘杰男，1970年5月出生于江苏省徐州市.现为北方工业大学信息学院教授、博士生导师.研究领域为机器学习、人工智能和知识图谱.
  曹梦远男，1998年5月出生河南省信阳市.现为中原工学院计算机学院硕士研究生，研究方向为自然语言处理.
- 基金信息：
  
  “新一代人工智能”国家科技重大专项(2020AAA0109703);国家自然科学基金(62076167;61772020;U23B2029);河南省高等学校重点科研项目(23A520022)
- DOI：10.12263/DZXB.20230623
  中图分类号： TP391
- 收稿：2023-07-03，
  
  修回：2024-01-26，
  
  纸质出版：2024-06-25
- 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

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