期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2012
卷号：2012
出版社：ACL Anthology
摘要：This paper presents a novel sequence labeling
model based on the latent-variable semi-
Markov conditional random fields for jointly
extracting argument roles of events from texts.
The model takes in coarse mention and type
information and predicts argument roles for a
given event template.
This paper addresses the event extraction
problem in a primarily unsupervised setting,
where no labeled training instances are available.
Our key contribution is a novel learning
framework called structured preference modeling
(PM), that allows arbitrary preference
to be assigned to certain structures during the
learning procedure. We establish and discuss
connections between this framework and other
existing works. We show empirically that the
structured preferences are crucial to the success
of our task. Our model, trained without
annotated data and with a small number
of structured preferences, yields performance
competitive to some baseline supervised approaches.
