Active Learning for Coreference Resolution

By Timothy Miller, Dmitriy Dligach, Guergana K. Savova. 2012

11 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. Jordan L. Boyd-Graber
2. Hal Daumé Iii
3. Mohit Iyyer
4. Yang Trista Cao
5. Anupam Guha
6. Danny Bouman
7. Yoav Goldberg
8. Mohit Bansal

Citations in the corpus

• Toward Gender-Inclusive Coreference Resolution (2019, methodology): “Considering the approaches used in dominant papers, the active learning ∨ ∧ 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].”
• Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers (2015, background): “We follow Miller et al. (2012), who use active learning for document-level coreference rather than at the mention level.”