@inproceedings{dong-etal-2022-syntactic,
title = "Syntactic Multi-view Learning for Open Information Extraction",
author = "Dong, Kuicai and
Sun, Aixin and
Kim, Jung-Jae and
Li, Xiaoli",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.272",
doi = "10.18653/v1/2022.emnlp-main.272",
pages = "4072--4083",
abstract = "Open Information Extraction (OpenIE) aims to extract relational tuples from open-domain sentences. Traditional rule-based or statistical models were developed based on syntactic structure of sentence, identified by syntactic parsers. However, previous neural OpenIE models under-explored the useful syntactic information. In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures. To better fuse heterogeneous information from the two graphs, we adopt multi-view learning to capture multiple relationships from them. Finally, the finetuned constituency and dependency representations are aggregated with sentential semantic representations for tuple generation. Experiments show that both constituency and dependency information, and the multi-view learning are effective.",
}
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<abstract>Open Information Extraction (OpenIE) aims to extract relational tuples from open-domain sentences. Traditional rule-based or statistical models were developed based on syntactic structure of sentence, identified by syntactic parsers. However, previous neural OpenIE models under-explored the useful syntactic information. In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures. To better fuse heterogeneous information from the two graphs, we adopt multi-view learning to capture multiple relationships from them. Finally, the finetuned constituency and dependency representations are aggregated with sentential semantic representations for tuple generation. Experiments show that both constituency and dependency information, and the multi-view learning are effective.</abstract>
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%0 Conference Proceedings
%T Syntactic Multi-view Learning for Open Information Extraction
%A Dong, Kuicai
%A Sun, Aixin
%A Kim, Jung-Jae
%A Li, Xiaoli
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F dong-etal-2022-syntactic
%X Open Information Extraction (OpenIE) aims to extract relational tuples from open-domain sentences. Traditional rule-based or statistical models were developed based on syntactic structure of sentence, identified by syntactic parsers. However, previous neural OpenIE models under-explored the useful syntactic information. In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures. To better fuse heterogeneous information from the two graphs, we adopt multi-view learning to capture multiple relationships from them. Finally, the finetuned constituency and dependency representations are aggregated with sentential semantic representations for tuple generation. Experiments show that both constituency and dependency information, and the multi-view learning are effective.
%R 10.18653/v1/2022.emnlp-main.272
%U https://aclanthology.org/2022.emnlp-main.272
%U https://doi.org/10.18653/v1/2022.emnlp-main.272
%P 4072-4083
Markdown (Informal)
[Syntactic Multi-view Learning for Open Information Extraction](https://aclanthology.org/2022.emnlp-main.272) (Dong et al., EMNLP 2022)
ACL
- Kuicai Dong, Aixin Sun, Jung-Jae Kim, and Xiaoli Li. 2022. Syntactic Multi-view Learning for Open Information Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4072–4083, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.