  Event extraction, the technology that aims to automatically get the
structural information from documents, has attracted more and more attention in
many fields. Most existing works discuss this issue with the token-level
multi-label classification framework by distinguishing the tokens as different
roles while ignoring the writing styles of documents. The writing style is a
special way of content organizing for documents and it is relative fixed in
documents with a special field (e.g. financial, medical documents, etc.). We
argue that the writing style contains important clues for judging the roles for
tokens and the ignorance of such patterns might lead to the performance
degradation for the existing works. To this end, we model the writing style in
documents as a distribution of argument roles, i.e., Role-Rank Distribution,
and propose an event extraction model with the Role-Rank Distribution based
Supervision Mechanism to capture this pattern through the supervised training
process of an event extraction task. We compare our model with state-of-the-art
methods on several real-world datasets. The empirical results show that our
approach outperforms other alternatives with the captured patterns. This
verifies the writing style contains valuable information that could improve the
performance of the event extraction task.
