DeepAI AI Chat
Log In Sign Up

Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks

by   Yue Wang, et al.
University of York
Central University of Finance and Economics
Huazhong University of Science u0026 Technology
University of Illinois at Chicago

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.


page 1

page 2

page 3

page 4


Writing Style Aware Document-level Event Extraction

Event extraction, the technology that aims to automatically get the stru...

Topical Keyphrase Extraction with Hierarchical Semantic Networks

Topical keyphrase extraction is used to summarize large collections of t...

Joint Extraction of Events and Entities within a Document Context

Events and entities are closely related; entities are often actors or pa...

COFFEE: A Contrastive Oracle-Free Framework for Event Extraction

Event extraction is a complex information extraction task that involves ...

Modeling Entities as Semantic Points for Visual Information Extraction in the Wild

Recently, Visual Information Extraction (VIE) has been becoming increasi...

Crowdsourced Collective Entity Resolution with Relational Match Propagation

Knowledge bases (KBs) store rich yet heterogeneous entities and facts. E...

Abnormal Event Detection via Hypergraph Contrastive Learning

Abnormal event detection, which refers to mining unusual interactions am...