基于分层次多粒度语义融合的中文事件检测

doi:10.11896/jsjkx.200800038

摘要/Abstract

摘要： 事件检测是信息抽取领域中一个重要的研究方向,其主要研究如何从非结构化自然语言文本中提取出事件的触发词,并识别出事件的类型。现有的基于神经网络的方法通常将事件检测看作单词的分类问题,但是这会引起中文事件检测触发词与文本中词语不匹配的问题。此外,由于中文词语的一词多义性,在不同的语境下,相同的词语可能会存在歧义性问题。针对中文事件检测中的这两个问题,提出了一个分层次多粒度语义融合的中文事件检测模型。首先,该模型利用基于字符序列标注的方法解决了触发词不匹配的问题,同时设计了字符-词语融合门机制,以获取多种分词结果中词语的语义信息;然后,通过设计字符-句子融合门机制,考虑整个句子的语义信息,学习序列的字-词-句混合表示,消除词语的歧义性;最后,为了平衡“O”标签与其他标签之间的数量差异,采用了带有偏差的损失函数对模型进行训练。在广泛使用的ACE2005数据集上进行了大量实验,实验结果表明,所提模型在精确率(Precision,P)、召回率(Recall,R)和F1值这3个指标上比现有的中文事件检测模型至少高出3.9%,1.4%和2.9%,证明了所提方法的有效性。

关键词: 多粒度语义融合, 卷积神经网络, 双向长短期记忆模型, 信息抽取, 预训练语言模型, 中文事件检测

Abstract: Event detection is an important task in information extraction field,which aims to identify trigger words in raw text and then classify them into correct event types.Neural network based methods usually regard event detection as a word-wise classification task,which suffers from the mismatch problem between words and triggers when applied to Chinese.Besides,due to the multiple word senses of a trigger word,the same trigger word in different sentences causes the ambiguity problem.To address the two problems in Chinese event detection,we propose a Chinese event detection model with hierarchical and multi-granularity semantic fusion.First,we adopt a character-based sequence labelling method to solve the mismatch problem,in which we devise a Character-Word Fusion Gate to capture the semantic information of words in different segmentation ways.Then we device a Character-Sentence Fusion Gate to learn a character-word-sentence hybrid representation of sequence,which takes the semantic information of the entire sentence into condition and solves the ambiguity problem.Finally,in order to balance the influence the label “O” and the other labels,a loss function with bias is applied to train our model.The experimental results on the widely used ACE2005 dataset show that our approach outperforms at least 3.9%,1.4% and 2.9% than other Chinese event detection models under the metrics of accuracy (Precision,P),recall (Recall,R) and F1.

Key words: Bidirectional long short-term memory model, Chinese event detection, Con-volutional neural network, Information extraction, Multi-granularity semantic fusion, Pre-trained language model

中图分类号:

TP182

丁玲, 向阳. 基于分层次多粒度语义融合的中文事件检测[J]. 计算机科学, 2021, 48(5): 202-208. https://doi.org/10.11896/jsjkx.200800038

DING Ling, XIANG Yang. Chinese Event Detection with Hierarchical and Multi-granularity Semantic Fusion[J]. Computer Science, 2021, 48(5): 202-208. https://doi.org/10.11896/jsjkx.200800038

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