Supervised Coreference Resolution Algorithm: Difference between revisions
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** an [[Unsupervised Coreference Resolution algorithm]]). | ** an [[Unsupervised Coreference Resolution algorithm]]). | ||
* <B>See:</B> [[Coreference Resolution algorithm]], [[Coreference Resolution System]], [[Supervised Machine Learning System]], [[Clustering Task]], [[Entity Mention Normalization System]], [[Natural Language Processing System]], [[Information Extraction System]]. | * <B>See:</B> [[Coreference Resolution algorithm]], [[Coreference Resolution System]], [[Supervised Machine Learning System]], [[Clustering Task]], [[Entity Mention Normalization System]], [[Natural Language Processing System]], [[Information Extraction System]]. | ||
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Latest revision as of 02:27, 24 September 2021
An Supervised Coreference Resolution Algorithm is a Data-Driven Coreference Resolution Algorithm that applies a Supervised Learning algorithm).
- Context:
- It can be applied by a Supervised Coreference Resolution System (to solve a Supervised Coreference Resolution Task).
- Example(s):
- Counter-Example(s):
- See: Coreference Resolution algorithm, 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.