Bidirectional Long Short-Term Memory Networks for Relation Classification

By Shu Zhang, Dequan Zheng, Xinchen Hu, Ming Yang. Pacific Asia Conference on Language, Information and Computation, 2015

521 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c68ec24e2f97c6875cd006a9fa2f0fbe934e4ae0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xu Han
2. Zhiyuan Liu
3. Maosong Sun
4. Tianyu Gao
5. Yankai Lin
6. Wen-tau Yih
7. Jianfeng Gao
8. Mohit Bansal

Citations in the corpus

• Cross-Sentence N-ary Relation Extraction with Graph LSTMs (2017, background): “, 2015) and RNN-based architectures (Zhang et al., 2015; Socher et al., 2012; Cai et al., 2016) have been successful.”
• Human Activity Recognition Using Recurrent Neural Networks (2017, background): “The terms i, f and o are named after their corresponding gates and c represents the memory cell [23].”
• Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (2016, methodology): “7 (Zhang et al., 2015) + PF + POS + NER + WNSYN + DEP 84.”
• Drug-Drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers (2017, background): “Zhang et al. [19] utilize long short-term memory network (LSTM), a typical RNN model, to represent sentence.”
• Relation Classification via Multi-Level Attention CNNs (2016, background): “Some more elaborate variants have been proposed to address this, including bidirectional LSTMs (Zhang et al., 2015), deep recurrent neural networks (Xu et al.”
• Multi-task Domain Adaptation for Sequence Tagging (2016, unknown): “…in NLP and have achieved impressive performance in problems such as sequence tagging (Lample et al., 2016; Yang et al., 2016; Ma and Hovy, 2016), relation classification (Xu et al., 2015; Zhang et al., 2015), and syntactic parsing (Kiperwasser and Goldberg, 2016; Cross and Huang, 2016).”
• More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, background): “Recently, with the development of deep learning, neural models have been widely adopted for RE (Zeng et al., 2014; Zhang et al., 2015) and achieved superior results.”