Joint Extraction of Events and Entities within a Document Context
This addresses the limitation of sentence-level extraction in information extraction for researchers and practitioners, though it is an incremental advancement over existing joint modeling approaches.
The paper tackles the problem of jointly extracting events and entities from documents by modeling their dependencies and performing document-level inference, demonstrating substantial performance improvements over state-of-the-art methods for event extraction and a strong baseline for entity extraction.
Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction.