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Open Information Extraction via Chunks
Conference proceeding

Open Information Extraction via Chunks

Kuicai Dong, Aixin Sun, Jung-Jae Kim and Xiaoli Li
2023 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2023), pp.15390-15404
01/01/2023

Abstract

Computer Science Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Linguistics Science & Technology Social Sciences Technology
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, ChunkOIE, for sentence chunking and tuple extraction on top of SaC. Chunk-OIE achieves state-of-the-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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