/awesome-relation-extraction

📖 A curated list of awesome resources dedicated to Relation Extraction, one of the most important tasks in Natural Language Processing (NLP).

Awesome Relation Extraction Awesome

awesome_re

A curated list of awesome resources dedicated to Relation Extraction, inspired by awesome-nlp and awesome-deep-vision.

Contributing: Please feel free to make pull requests.

Contents

Research Trends and Surveys

Papers

Supervised Approaches

CNN-based Models

  • Convolution Neural Network for Relation Extraction [paper] [code] [review]
    • ChunYang Liu, WenBo Sun, WenHan Chao and WanXiang Che
    • ADMA 2013
  • Relation Classification via Convolutional Deep Neural Network [paper] [code] [review]
    • Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou and Jun Zhao
    • COLING 2014
  • Relation Extraction: Perspective from Convolutional Neural Networks [paper] [code] [review]
    • Thien Huu Nguyen and Ralph Grishman
    • NAACL 2015
  • Classifying Relations by Ranking with Convolutional Neural Networks [paper] [code]
    • Cicero Nogueira dos Santos, Bing Xiang and Bowen Zhou
    • ACL 2015
  • Attention-Based Convolutional Neural Network for Semantic Relation Extraction [paper]
    • Yatian Shen and Xuanjing Huang
    • COLING 2016
  • Relation Classification via Multi-Level Attention CNNs [paper] [code]
    • Linlin Wang, Zhu Cao, Gerard de Melo and Zhiyuan Liu
    • ACL 2016
  • MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks [paper]
    • Ji Young Lee, Franck Dernoncourt and Peter Szolovits
    • SemEval 2017

RNN-based Models

  • Relation Classification via Recurrent Neural Network [paper]
    • Dongxu Zhang and Dong Wang
    • arXiv 2015
  • Bidirectional Long Short-Term Memory Networks for Relation Classification [paper]
    • Shu Zhang, Dequan Zheng, Xinchen Hu and Ming Yang
    • PACLIC 2015
  • End-to-End Relation Extraction using LSTMs on Sequences and Tree Structure [paper]
    • Makoto Miwa and Mohit Bansal
    • ACL 2016
  • Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification [paper] [code]
    • Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao and Bo Xu
    • ACL 2016
  • Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention [paper]
    • Minguang Xiao and Cong Liu
    • COLING 2016
  • Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing [paper]
    • Joohong Lee, Sangwoo Seo and Yong Suk Choi
    • summitted to SDM 2018

Dependency-based Models

  • Semantic Compositionality through Recursive Matrix-Vector Spaces [paper] [code]
    • Richard Socher, Brody Huval, Christopher D. Manning and Andrew Y. Ng
    • EMNLP-CoNLL 2012
  • Factor-based Compositional Embedding Models [paper]
    • Mo Yu, Matthw R. Gormley and Mark Dredze
    • NIPS Workshop on Learning Semantics 2014
  • A Dependency-Based Neural Network for Relation Classification [paper]
    • Yang Liu, Furu Wei, Sujian Li, Heng Ji, Ming Zhou and Houfeng Wang
    • ACL 2015
  • Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path [paper] [code]
    • Xu Yan, Lili Mou, Ge Li, Yunchuan Chen, Hao Peng and Zhi Jin * EMNLP 2015
  • Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling [paper]
    • Kun Xu, Yansong Feng, Songfang Huang and Dongyan Zhao
    • EMNLP 2015
  • Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation [paper]
    • Yan Xu, Ran Jia, Lili Mou, Ge Li, Yunchuan Chen, Yangyang Lu and Zhi Jin
    • COLING 2016
  • Bidirectional Recurrent Convolutional Neural Network for Relation Classification [paper]
    • Rui Cai, Xiaodong Zhang and Houfeng Wang
    • ACL 2016

Distant Supervision Approaches

  • Distant supervision for relation extraction without labeled data [paper] [review]
    • Mike Mintz, Steven Bills, Rion Snow and Dan Jurafsky
    • ACL 2009
  • Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations [paper] [code]
    • Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer and Daniel S. Weld
    • ACL 2011
  • Multi-instance Multi-label Learning for Relation Extraction [paper] [code]
    • Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati and Christopher D. Manning
    • EMNLP-CoNLL 2012
  • Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks [paper] [review]
    • Daojian Zeng, Kang Liu, Yubo Chen and Jun Zhao
    • EMNLP 2015
  • Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks [paper] [review]
    • Xiaotian Jiang, Quan Wang, Peng Li, Bin Wang
    • COLING 2016
  • Incorporating Relation Paths in Neural Relation Extraction [paper] [review]
    • Wenyuan Zeng, Yankai Lin, Zhiyuan Liu and Maosong Sun
    • EMNLP 2017
  • Neural Relation Extraction with Selective Attention over Instances [paper] [code]
    • Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan and Maosong Sun
    • ACL 2017
  • Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text [paper] [code] [code]
    • Desh Raj, Sunil Kumar Sahu and Ashish Anan
    • CoNLL 2017
  • RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information [paper] [code]
    • Shikhar Vashishth, Rishabh Joshi, Sai Suman Prayaga, Chiranjib Bhattacharyya and Partha Talukdar
    • EMNLP 2018

Miscellaneous

  • Jointly Extracting Relations with Class Ties via Effective Deep Ranking [paper]
    • Hai Ye, Wenhan Chao, Zhunchen Luo and Zhoujun Li
    • ACL 2017
  • End-to-End Neural Relation Extraction with Global Optimization [paper]
    • Meishan Zhang, Yue Zhang and Guohong Fu
    • EMNLP 2017
  • Adversarial Training for Relation Extraction [paper]
    • Yi Wu, David Bamman and Stuart Russell
    • EMNLP 2017
  • A neural joint model for entity and relation extraction from biomedical text[paper]
    • Fei Li, Meishan Zhang, Guohong Fu and Donghong Ji
    • BMC bioinformatics 2017
  • Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning [paper]
    • Yuntian Feng, Hongjun Zhang, Wenning Hao, and Gang Chen
    • Journal of Computational Intelligence and Neuroscience 2017

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Datasets

  • SemEval-2010 Task 8 [paper] [download]
    • Multi-Way Classification of Semantic Relations Between Pairs of Nominals
  • New York Times (NYT) Corpus [paper] [download]
    • This dataset was generated by aligning Freebase relations with the NYT corpus, with sentences from the years 2005-2006 used as the training corpus and sentences from 2007 used as the testing corpus.

For state of the art results check out nlpprogress.com on relation extraction

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Videos and Lectures

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Systems

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License

license

To the extent possible under law, Joohong Lee has waived all copyright and related or neighboring rights to this work.