Target-dependent event detection from news

doi:10.3969/j.issn.1000-5641.2023.02.008

Abstract

Abstract:

In real-world scenarios, various events in the news are not only too nuanced and complex to distinguish, but also involve multiple entities. To address these problems, previous event-centric methods are designed to detect events first and then extract arguments, relying on imperfect performance for event trigger detection; this process, however, is unfit to deal with the sheer volume of news in the real world. Given that the performance of named entity recognition (NER) is satisfactory, we shift our perspective from an event-centric to a target-centric view. This paper proposes a new task: target-dependent event detection (TDED), which aims to extract target entities and detect their corresponding events. We also propose a semantic and syntactic aware approach to support thousands of target entity extractions first and subsequently the detection of dozens of event types; this approach can be applied to data from massive corporations. Experimental results on a real-world Chinese financial dataset demonstrated that our model outperformed previous methods, particularly in complex scenarios.

Key words: target-dependent, event detection, entity recognition, event keywords, syntactic dependency distance

CLC Number:

TP391

Tiantian ZHANG, Man LAN. Target-dependent event detection from news[J]. Journal of East China Normal University(Natural Science), 2023, 2023(2): 60-72.

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数据集	事件类型数/种	数据切分	句子数/个	平均词汇数/个	平均事件数/件
FinNewsCN	52	训练集	5695	69.83	1.36
		验证集	707	70.78	1.45
		测试集	1239	78.10	1.49
ACE 2005	33	训练集	17172	18.88	0.25
		验证集	923	23.28	0.49
		测试集	823	27.01	0.48

模型	P_P/%			R_R/%			$ {F}_{1}/\mathrm{\%} $
模型	目标实体抽取	事件识别	整体抽取	目标实体抽取	事件识别	整体抽取	目标实体抽取	事件识别	整体抽取
QA-SL^[13]	83.65	83.33	83.49	78.46	81.88	80.13	64.13	67.81	65.92
QA-SL变体	91.39	95.21	93.26	79.71	78.50	79.10	70.85	74.73	72.73
端到端序列标注模型^[13]	88.55	93.53	90.97	68.12	84.41	75.39	59.47	76.94	67.09
Span_pipeline^[14]	88.54	75.57	81.54	78.34	72.02	75.05	74.35	58.17	65.28
Span_joint^[14]	86.98	76.09	81.17	68.53	56.06	61.67	60.73	43.28	50.54
AEN-BERT^[25]	91.39	95.21	93.26	83.29	71.36	76.86	68.10	67.72	67.91
LCF-BERT^[23]	91.39	95.21	93.26	85.79	85.01	85.40	74.83	79.11	76.91
ESSM_pipeline	91.39	95.21	93.26	87.89	84.53	86.17	77.58	79.66	78.61
ESSM_joint	90.50	94.84	92.62	86.05	84.22	85.13	72.27	77.26	74.68

模型	P_P/%	R_R/%	$ {F}_{1}/\% $
Span^[14]	84.51	78.91	81.61
AEN-BERT^[25]	71.60	68.07	69.79
LCF-BERT^[23]	81.60	82.66	82.12
ESSM	83.74	84.43	84.08

复杂样本情况	F₁/%
复杂样本情况	LCF-BERT^[23]	ESSM
1 实体参与 1 事件	84.51	78.91
1 实体参与 2 事件	71.60	68.07
1 实体参与 > 2 事件	81.60	82.66
多个实体	83.74	84.43

模型	P_P/%	R_R/%	${F}_{1}/\%$
LCF-BERT^[23]	81.60	82.66	82.12
ESSM-KWFeature	83.97	83.21	83.59
ESSM-Syntactic	84.98	82.77	83.86
ESSM	83.74	84.43	84.08