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

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