29,393 research outputs found
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Joint extraction of entities and relations is an important task in
information extraction. To tackle this problem, we firstly propose a novel
tagging scheme that can convert the joint extraction task to a tagging problem.
Then, based on our tagging scheme, we study different end-to-end models to
extract entities and their relations directly, without identifying entities and
relations separately. We conduct experiments on a public dataset produced by
distant supervision method and the experimental results show that the tagging
based methods are better than most of the existing pipelined and joint learning
methods. What's more, the end-to-end model proposed in this paper, achieves the
best results on the public dataset
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Most existing event extraction (EE) methods merely extract event arguments
within the sentence scope. However, such sentence-level EE methods struggle to
handle soaring amounts of documents from emerging applications, such as
finance, legislation, health, etc., where event arguments always scatter across
different sentences, and even multiple such event mentions frequently co-exist
in the same document. To address these challenges, we propose a novel
end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic
graph to fulfill the document-level EE (DEE) effectively. Moreover, we
reformalize a DEE task with the no-trigger-words design to ease the
document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we
build a large-scale real-world dataset consisting of Chinese financial
announcements with the challenges mentioned above. Extensive experiments with
comprehensive analyses illustrate the superiority of Doc2EDAG over
state-of-the-art methods. Data and codes can be found at
https://github.com/dolphin-zs/Doc2EDAG.Comment: Accepted by EMNLP 201
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