- Matching the Blanks: Distributional Similarity for Relation Learning [ACL 2019] Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, Tom Kwiatkowski.
- How to solve relation extraction task by using BERT
- three representation
- standard BERT
- add Position embed
- Entity Mark
- three output
- CLS output
- Mention level pooling
- Mention start
- random replace the Entity ->
[BLANK] - negative sample
- Inducing Relational Knowledge from BERT [AAAI 2020] Zied Bouraoui, Jose Camacho-Collados, Steven Schockaert.
- using the middle sentence to represent the relation of the pair.
- choose one temperate to do the cloze task.
- Entity-Relation Extraction as Multi-Turn Question Answering [ACL 2019] Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li.
- Start-end + Multi-time => RL. The model also a pipeline model.
- Twenty-铿乿e years of information extraction [NLE 2019] Ralph Grishman
- From Three Corpus to introduce the problem and method in Information Extraction IE in past 25 years.
- Improving Entity Linking by Modeling Latent Entity Type Information [AAAI 2020] Shuang Chen, Jinpeng Wang, Feng Jiang, Chin-Yew Lin.
- Improve the embedding by [MASK]
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