Open_Information_Extraction.md

Open Information Extraction

Papers

• paper: Niklaus, C. , Cetto, M. , Freitas, André, & Handschuh, S. . (2018). A survey on open information extraction.
• paper: Etzioni, O. , Fader, A. , Christensen, J. , Soderland, S. , & Mausam. (2012). Open Information Extraction: The Second Generation. International Joint Conference on Ijcai. DBLP.
• paper: Saha Swarnadeep, Pal Harinder, Mausam. (2017). Bootstrapping for Numerical Open IE. 10.18653/v1/P17-2050.
• paper: 王莉峰. (0). 领域自适应的中文实体关系抽取研究. (Doctoral dissertation, 哈尔滨工业大学).
• slide: Sina Miran. Brief Introduction and Review of Open Information Extraction (Open-IE) Systems. CS 290D Project Presentation.
• paper: 赵 军, 刘 康, 周光有, 蔡 黎. 开放式文本信息抽取[J]. 中文信息学报, 2011, 25(6): 98-111.
• paper: 秦兵, 刘安安, & 刘挺. (2015). 无指导的中文开放式实体关系抽取. 计算机研究与发展, 52(5), 1029-1035.

Open Information Extraction (OIE) Resources

• link: https://github.com/gkiril/oie-resources
• author: Kiril Gashteovski
• note: a collection of papers for oie tasks.

KnowItAll

• link: https://github.com/knowitall
• web: http://projectsweb.cs.washington.edu/research/knowitall/
• author: The University of Washington's Turing Center
• note: a project for open information extraction.
• extra: ReVerb, OLLIE.

CoreNLP(OpenIE)

• link: https://stanfordnlp.github.io/CoreNLP/openie.html
• author: Stanford

OpenIE-standalone

• link: http://openie.allenai.org/
• author: allennlp
• extra: OpenIE-5(the latest version).

Stanford-OpenIE-Python

• link: https://github.com/philipperemy/Stanford-OpenIE-Python
• author: Philippe Rémy
• note: a python wrapper for Stanford OpenIE system.

DeepIE

• link: https://github.com/loujie0822/DeepIE
• author: loujie0822
• note: information extraction based on deep learning, including papers and articles.

Supervised-OIE

• link: https://github.com/gabrielStanovsky/supervised-oie
• author: Gabriel Stanovsky
• note: code for training a neural OpenIE model published in NAACL 2018.

IMOJIE

• link: https://github.com/dair-iitd/imojie
• author: Data Analytics and Intelligence Research (DAIR) Group, IIT Delhi
• note: neural generation model for open information extraction in ACL 2020.

AutoIE

• link: https://github.com/ZhuiyiTechnology/AutoIE
• author: Zhuiyi Technology
• note: a task to build IE system with noise and incomplete annotations.

OpenHowNet

• link: https://github.com/thunlp/OpenHowNet
• author: THUNLP
• web: https://openhownet.thunlp.org/
• note: a project contains core data of HowNet and OpenHowNet API developed by THUNLP, which provides a convenient way to search information in HowNet, display sememe trees, calculate word similarity via sememes, etc.

UIE

• link: https://universal-ie.github.io
• paper: Lu, Y. , Liu, Q. , Dai, D. , Xiao, X. , Lin, H. , & Han, X. , et al. (2022). Unified structure generation for universal information extraction.
• code
• extra: UIE-PyTorch
• blog:
  • 首创基于Prompt的产业级信息抽取能力，这个开源工具用心了！
  • 信息抽取大一统：百度中科院发布通用抽取模型UIE，刷新13个IE数据集SOTA！
  • 抽取式UIE的医学命名实体识别实践

OIE-Benchmark

• link: https://github.com/gabrielStanovsky/oie-benchmark
• author: Gabriel Stanovsky
• note: framework for converting QA-SRL to Open-IE and evaluating Open IE parsers.

PyTorch-IE

• link: https://github.com/ChristophAlt/pytorch-ie
• author: Christoph Alt
• note: state-of-the-art information extraction in pytorch.