Active Learning for Coreference Resolution

By Florian Laws, Florian Heimerl, Hinrich Schütze. 2012

23 citations recorded by the source corpus.

Subfields: Natural Language Processing

How researchers see this paper

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

In Natural Language Processing

1. Yang Trista Cao
2. Hal Daumé Iii
3. Eric P. Xing
4. Mrinmaya Sachan
5. Eduard H. Hovy
6. Mohit Bansal
7. Yejin Choi
8. Graham Neubig

Citations in the corpus

• Toward Gender-Inclusive Coreference Resolution (2019, methodology): “algorithms used are not statistically unbiased [Guha et al. 2015; Laws et al. 2012; Miller et al. 2012; Ng and Cardie 2003; Sachan et al. 2015].”
• Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, methodology): “Considering the approaches used in dominant papers, the active learning algorithms used are not statistically unbiased (Ng and Cardie 2003; Laws, Heimerl, and Schütze 2012; Miller, Dligach, and Savova 2012; Sachan, Hovy, and Xing 2015; Guha et al. 2015).”
• An Active Learning Approach to Coreference Resolution (2015, background): “To the best of our knowledge, the only works on active learning for coreference resolution have been [Gasperin, 2009], [Laws et al., 2012] and [Miller et al., 2012].”