  Joint-event-extraction, which extracts structural information (i.e., entities
or triggers of events) from unstructured real-world corpora, has attracted more
and more research attention in natural language processing. Most existing works
do not fully address the sparse co-occurrence relationships between entities
and triggers, which loses this important information and thus deteriorates the
extraction performance. To mitigate this issue, we first define the
joint-event-extraction as a sequence-to-sequence labeling task with a tag set
composed of tags of triggers and entities. Then, to incorporate the missing
information in the aforementioned co-occurrence relationships, we propose a
Cross-Supervised Mechanism (CSM) to alternately supervise the extraction of
either triggers or entities based on the type distribution of each other.
Moreover, since the connected entities and triggers naturally form a
heterogeneous information network (HIN), we leverage the latent pattern along
meta-paths for a given corpus to further improve the performance of our
proposed method. To verify the effectiveness of our proposed method, we conduct
extensive experiments on four real-world datasets as well as compare our method
with state-of-the-art methods. Empirical results and analysis show that our
approach outperforms the state-of-the-art methods in both entity and trigger
extraction.
