A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression

Mingming Sun, Wenyue Hua, Zoey Liu, Xin Wang, Kangjie Zheng, Ping Li


Abstract
Existing OIE (Open Information Extraction) algorithms are independent of each other such that there exist lots of redundant works; the featured strategies are not reusable and not adaptive to new tasks. This paper proposes a new pipeline to build OIE systems, where an Open-domain Information eXpression (OIX) task is proposed to provide a platform for all OIE strategies. The OIX is an OIE friendly expression of a sentence without information loss. The generation procedure of OIX contains shared works of OIE algorithms so that OIE strategies can be developed on the platform of OIX as inference operations focusing on more critical problems. Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly increased. This paper focuses on the task of OIX and propose a solution – Open Information Annotation (OIA). OIA is a predicate-function-argument annotation for sentences. We label a data set of sentence-OIA pairs and propose a dependency-based rule system to generate OIA annotations from sentences. The evaluation results reveal that learning the OIA from a sentence is a challenge owing to the complexity of natural language sentences, and it is worthy of attracting more attention from the research community.
Anthology ID:
2020.emnlp-main.167
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2140–2150
Language:
URL:
https://aclanthology.org/2020.emnlp-main.167
DOI:
10.18653/v1/2020.emnlp-main.167
Bibkey:
Cite (ACL):
Mingming Sun, Wenyue Hua, Zoey Liu, Xin Wang, Kangjie Zheng, and Ping Li. 2020. A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2140–2150, Online. Association for Computational Linguistics.
Cite (Informal):
A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression (Sun et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.167.pdf
Video:
 https://slideslive.com/38939349