@inproceedings{dong-etal-2023-open,
title = "Open Information Extraction via Chunks",
author = "Dong, Kuicai and
Sun, Aixin and
Kim, Jung-jae and
Li, Xiaoli",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.951",
doi = "10.18653/v1/2023.emnlp-main.951",
pages = "15390--15404",
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-of-the-art results on multiple OIE datasets, showing that SaC benefits the OIE task.",
}
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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 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-of-the-art results on multiple OIE datasets, showing that SaC benefits the OIE task.</abstract>
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%0 Conference Proceedings
%T Open Information Extraction via Chunks
%A Dong, Kuicai
%A Sun, Aixin
%A Kim, Jung-jae
%A Li, Xiaoli
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F dong-etal-2023-open
%X 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-of-the-art results on multiple OIE datasets, showing that SaC benefits the OIE task.
%R 10.18653/v1/2023.emnlp-main.951
%U https://aclanthology.org/2023.emnlp-main.951
%U https://doi.org/10.18653/v1/2023.emnlp-main.951
%P 15390-15404
Markdown (Informal)
[Open Information Extraction via Chunks](https://aclanthology.org/2023.emnlp-main.951) (Dong et al., EMNLP 2023)
ACL
- Kuicai Dong, Aixin Sun, Jung-jae Kim, and Xiaoli Li. 2023. Open Information Extraction via Chunks. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15390–15404, Singapore. Association for Computational Linguistics.