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Open Information Extraction via Chunks

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abstract

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.

fields

cs.CL 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Semantically Cohesive Word Grouping in Indian Languages

cs.CL · 2025-01-07 · conditional · novelty 5.0

Word grouping of Hindi text by semantic units improves cross-lingual structural uniformity and yields small consistent machine translation gains under decomposed prompting.

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  • Semantically Cohesive Word Grouping in Indian Languages cs.CL · 2025-01-07 · conditional · none · ref 2023 · internal anchor

    Word grouping of Hindi text by semantic units improves cross-lingual structural uniformity and yields small consistent machine translation gains under decomposed prompting.