CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Automatically Labeled Data Generation for Large Scale Event Extraction
Chen Yubo; Liu Shulin; Zhang Xiang; Liu Kang; Zhao Jun
Conference NameAnnual Meeting of Association for Computational Linguistics (ACL 2017)
Conference DateJuly 30 - August 4
Conference PlaceVancouver, Canada

Traditional approaches to the task of ACE
event extraction primarily rely on elaborately
designed features and complicated
natural language processing (NLP) tools.
These traditional approaches lack generalization,
take a large amount of human
effort and are prone to error propagation
and data sparsity problems. This
paper proposes a novel event-extraction
method, which aims to automatically extract
lexical-level and sentence-level features
without using complicated NLP
tools. We introduce a word-representation
model to capture meaningful semantic regularities
for words and adopt a framework
based on a convolutional neural network
(CNN) to capture sentence-level clues.
However, CNN can only capture the most
important information in a sentence and
may miss valuable facts when considering
multiple-event sentences. We propose a
dynamic multi-pooling convolutional neural
network (DMCNN), which uses a dynamic
multi-pooling layer according to
event triggers and arguments, to reserve
more crucial information. The experimental
results show that our approach significantly
outperforms other state-of-the-art

Indexed ByEI
Document Type会议论文
AffiliationNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chen Yubo,Liu Shulin,Zhang Xiang,et al. Automatically Labeled Data Generation for Large Scale Event Extraction[C],2017.
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