Open Information Extraction via Chunks
Kuicai Dong Aixin Sun Jung-Jae Kim Xiaoli Li
Open Information Extraction (OIE) aims to extract relational tuples from
open-domain sentences. Existing OIE systems split a sentence into tokens and
recognize token spans as tuple relations and arguments. We instead propose
Sentence as Chunk sequence (SaC) and recognize chunk spans as tuple relations
and arguments. We argue that SaC has better quantitative and qualitative
properties for OIE than sentence as token sequence, and evaluate four choices
of chunks (i.e., CoNLL chunks, simple phrases, NP chunks, and spans from
SpanOIE) against gold OIE tuples. Accordingly, we propose a simple BERT-based
model for sentence chunking, and propose Chunk-OIE for tuple extraction on top
of SaC. Chunk-OIE achieves state-of-the-art results on multiple OIE datasets,
showing that SaC benefits OIE task.