南京大学学报(自然科学版) ›› 2023, Vol. 59 ›› Issue (3): 460–470.doi: 10.13232/j.cnki.jnju.2023.03.009

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基于多粒度信息编码和联合优化的篇章级服务事件序列抽取方法

程钦男1, 莫志强1, 曹斌1(), 范菁1, 单宇翔2   

  1. 1.浙江工业大学计算机科学与技术学院，杭州，310023
    2.浙江中烟工业有限责任公司信息中心，杭州，310009
  • 收稿日期:2023-03-30 出版日期:2023-05-31 发布日期:2023-06-09
  • 通讯作者: 曹斌 E-mail:bincao@zjut.edu.cn
  • 基金资助:
    国家自然基金(62276233);浙江省科技计划(2023C01048)

Document⁃level service event sequence extraction based on multi⁃granularity information encoding and joint optimization

Qinnan Cheng1, Zhiqiang Mo1, Bin Cao1(), Jing Fan1, Yuxiang Shan2   

  1. 1.College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, 310023, China
    2.Information Center, China Tobacco Zhejiang Industrial Co. , Ltd. , Hangzhou, 310009, China
  • Received:2023-03-30 Online:2023-05-31 Published:2023-06-09
  • Contact: Bin Cao E-mail:bincao@zjut.edu.cn

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摘要：

篇章级别的服务事件序列抽取任务旨在发现给定服务的相关文本中所有服务事件的顺序序列关系，构建得到一组按照服务事件发生顺序排列的服务事件集合，其研究可以广泛应用于知识图谱构建、自动问答等任务.与该任务相关的现有工作分过程抽取和事件时序关系抽取两类：过程抽取相关研究默认事件真实发生的顺序与文本描述的顺序一致，忽略了许多非过程性文本中事件发生的顺序与文本描述顺序不一致的情况；.事件时序关系抽取的相关研究往往关注事件对之间的时序关系判断，无法建模所有事件的顺序序列关系.针对以上问题，提出一种基于多粒度信息编码和联合优化的篇章级服务事件序列抽取方法，使用多粒度信息编码模块获得服务文本中具有丰富语义信息的服务事件向量表示，再利用联合优化模块提取服务事件之间的顺序序列关系，得到篇章级别的服务事件序列.由于没有公开数据集可以直接用于服务事件序列抽取任务的评估，抽取基于事件时序关系抽取的公开数据集TimeBank （TB），AQUAINT （AQ），Platinum （PL）和MATRES中的数据，构建了可用于篇章级服务事件序列抽取任务评估的数据集，实验结果证明了提出方法的有效性.

关键词: 服务文本, 服务事件, 序列抽取, 多粒度编码, 联合优化

Abstract:

The task of extracting a sequence of service events at the document level aims to discover the sequential relationship of all service events in given service?related texts，and to construct a set of service events arranged in order of occurrence. The research can be widely applied to tasks such as knowledge graph construction and automatic question answering. Existing works related to this task can be divided into two categories: process extraction and event temporal relation extraction. Researches on process extraction assume that the true order of events is consistent with the order of text description，ignoring the fact that in many non?process texts，the order of events may not be consistent with the description order. Related researches on event temporal relation extraction often focuse on judging the temporal relation between event pairs and cannot model the sequential relationship of all events. A document?level service event sequence extraction method based on multi?granularity information coding and joint optimization is proposed to solve above problems. A multi?granularity information coding module is used to learn the vector representation of service events in the service text. Then，a joint optimization module is used to extract the service event sequence relation to obtain the document?level service event sequence. Considering that there is no public dataset directly used to evaluate the service event sequence extraction task，this paper constructs a dataset based on the event temporal relation extraction public datasets TimeBank (TB)，AQUAINT (AQ)，Platinum (PL) and MATRES. Experimental results show the effectiveness of the method proposed in this paper.

Key words: service text, service event, sequence extraction, multi?granularity coding, joint optimization

中图分类号: 

  • TP391

图1

服务事件序列抽取任务"

图2

本文模型的架构图"

表1

本文构造的数据集信息"

标题文本内容事件序列标签
ABC19980114.1830.0611.tmlIt just went down.The controller at Boston center triese21+OCCURRENCE+262
to raise hundired. If you hear center, There is no respe30+OCCURRENCE+269
onse. Later, the controller asks the Easewind pilot fore33+PERCEPTION+270
more details. Ah yes, sir. It just blew up in the air, ande34+OCCURRENCE+271
……from ah, coming up from that.e36+OCCURRENCE+272

表2

相关数据集的统计信息"

Dataset#Docs#Events#ES
TB1832583-
AQ721885-
PL20324-
Ours84594961496

表3

局部视角对比实验的结果"

方法AccuracyPrecisionRecallF1⁃score
Ours90.95%88.68%86.43%87.54%
Text order75.31%---
ListNet82.72%---
BiLSTM⁃Based Pairwise87.86%84.21%82.42%83.30%

表4

全局视角对比实验的结果"

MethodLCS_Rate
Ours88.83%
Text order70.89%
ListNet81.53%
BiLSTM⁃based pairwise82.67%

表5

局部视角的消融实验结果"

方法AccuracyPrecisionRecallF1⁃score
本文方法90.95%88.68%86.43%87.54%
去除MHA89.09%85.66%84.64%85.15%
去除BiLSTM89.71%86.59%85.33%85.96%
去除Listwise88.89%86.28%82.82%84.51%
去除ILP85.31%83.57%80.16%81.82%

表6

全局视角的消融实验结果"

方法LCS
本文方法88.83%
去除MHA86.15%
去除BiLSTM85.76%
去除Listwise85.80%
去除 ILP80.41%
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