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EMNLP2023Top-tier venue

Open Information Extraction via Chunks

Kuicai Dong, Aixin Sun, Jung-Jae Kim, Xiaoli Li

2023Year
4Citations
5Top-tier citations

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 properties for OIE than sentence as token sequence, and evaluate four choices of chunks (i.e., CoNLL chunks, OIA simple phrases, noun phrases, and spans from SpanOIE). Also, we propose a simple end-to-end BERT-based model, Chunk-OIE, for sentence chunking and tuple extraction on top of SaC. Chunk-OIE achieves state-ofthe-art results on multiple OIE datasets, showing that SaC benefits the OIE task. Our model will be publicly available in Github upon paper acceptance.

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