Supervised Coreference Resolution Task
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A Supervised Coreference Resolution Task is a data-driven coreference resolution task that is a supervised classification task.
- Context:
- It can be solved by a Supervised Coreference Resolution System (that implements a Supervised Coreference Resolution Algorithm.
- It can range from being a Fully-Supervised Coreference Resolution Task to being a Semi-Supervised Coreference Resolution Task.
- Example(s):
- Counter-Example(s):
- See: Supervised Graph-Node Linking Task, Coreference Resolution System, Supervised Machine Learning System, Clustering Task, Entity Mention Normalization System, Natural Language Processing System, Information Extraction System.
References
2011
- (Zheng et al., 2011) ⇒ Jiaping Zheng, Wendy W. Chapman, Rebecca S. Crowley, and Guergana K. Savova. (2011). “Coreference Resolution: A Review of General Methodologies and Applications in the Clinical Domain.” In: Journal of Biomedical Informatics, 44(6). doi:10.1016/j.jbi.2011.08.006
- QUOTE: In the mid-1990s, methods for performing supervised coreference resolution sprang up. The widespread availability of the MUC and ACE corpora further shaped the research community to move towards statistical approaches. Complete heuristics-based systems gradually saw a decline of interest in the community, although isolated rules are still employed to encode hard linguistic constraints. Two types of machine learning methods emerged—a two-step binary classification followed by clustering and a ranking approach. The key distinction between them is that the binary classification approach makes coreference decisions on the antecedent candidates independently of each other, while the ranking approach takes into account other antecedent candidates.