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Open Information eXpression

Open Information eXpression (OIX) is proposed as a methodology to highly adaptable Open Information Extraction systems [1]. Instead of Extraction, it focuses on eXpression, that is, expressing all the information in the language into an intermediate structure so that one can extract various types of information for different applications from the intermediate structure. Open Information Annotation (OIA) is our implementation of the OIX methodology.

Open Information Annotation

Open Information Annotation (OIA)[2] is an implementation of Open Information eXpression (OIX). It expresses a sentence into a Predicate/Function-Arguments structure.

Standards

The OIA standard has evolved in two versions:

  • v 1.0: described in our EMNLP 2020 paper [1], with annotation guideline.
  • v 1.1: described in our ACL 2022 paper [2], which extend the set of node types.

Data

We construct our OIA data set (in standard v 1.1) as described in our ACL 2022 paper [2]. It can be found in the data folder.

Format

See OIA Data Format for details.

License/Copyright

The annotations and database rights of the OIA Dataset are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

You should have received a copy of the license along with this work. If not, see http://creativecommons.org/licenses/by-sa/4.0/.

The underlying texts come from where the sentences are from English-EWT (version 2.4) of the Universal Dependency Dataset.

Citation

Please cite the following papers if you want to use this data set:

@inproceedings{sun2020predicate,
  title={A Predicate-Function-Argument Annotation of Natural Language for Open-Domain Information eXpression},
  author={Sun, Mingming and Hua, Wenyue and Liu, Zoey and Wang, Xin and Zheng, Kangjie and Li, Ping},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  pages={2140--2150},
  year={2020},
  doi = "10.18653/v1/2020.emnlp-main.167",
}

@inproceedings{wangxin2022oie@oia,
  title={OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework},
  author={Wang, Xin and Peng, Minlong and Sun, Mingming and Li, Ping},
  booktitle={Proceedings of the 2022 Conference on Association for Computational Linguistics (ACL)},
  year={2022},
}

Reference

[1] 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.

[2] Xin Wang, Minlong Peng, Mingming Sun, Ping Li. 2022. OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework. In Proceedings of the 2022 Conference on Association for Computational Linguistics (ACL).