IE Papers

Introductory & Classics

  • Appelt, D.E., & Israel, D.J. (1999). Introduction to Information Extraction Technology. International Joint Conference on Artificial Intelligence. Paper

Named Entity Recognition

Surveys

Classics

Models

Entity Linking

Surveys

Classics

Models

Relation Extraction

Surveys

  • Hogan, W. (2022). An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods. ArXiv, abs/2207.08286. Paper

Classics

  • Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. Distant Supervision for Relation Extraction Without Labeled Data. In Proc. of ACL-IJCNLP, pages 1003–1011, 2009. Paper
  • Sebastian Riedel, Limin Yao, and Andrew McCallum. Modeling Relations and Their Mentions without Labeled Text. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD ’10), 2010. Paper
  • Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. Position-aware attention and supervised data improve slot filling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 35–45, Copenhagen, Denmark, September 2017. Association for Computational Linguistics. Paper

Models

  • Alt, C., Hübner, M., & Hennig, L. (2019). Improving Relation Extraction by Pre-trained Language Representations. ArXiv, abs/1906.03088. Paper
  • Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019. Matching the Blanks: Distributional Similarity for Relation Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2895–2905, Florence, Italy. Association for Computational Linguistics. Paper
  • Jiale Han, Shuai Zhao, Bo Cheng, Shengkun Ma, and Wei Lu. 2022. Generative Prompt Tuning for Relation Classification. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3170–3185, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. Paper
  • Yuxuan Chen, David Harbecke, and Leonhard Hennig. 2022. Multilingual Relation Classification via Efficient and Effective Prompting. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1059–1075, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. Paper