Supervised Coreference Resolution System
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A Supervised Coreference Resolution System is a Coreference Resolution System that is based on Supervised Machine Learning System.
- Context:
- It can solve a Supervised Coreference Resolution Task by implementing a Supervised Coreference Resolution Algorithm.
- Example(s):
- Counter-Example(s):
- See: 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.
2010
- (Ng, 2010) ⇒ Vincent Ng. (2010). “Supervised Noun Phrase Coreference Research: The First Fifteen Years.” In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL 2010).
- QUOTE: Once a training set is created, we can train a coreference model using an off-the-shelf learning algorithm. Decision tree induction systems (e.g., C5 (Quinlan, 1993)) are the first and one of the most widely used learning algorithms by coreference researchers, although rule learners (e.g., RIPPER (Cohen, 1995)) and memory-based learners (e.g., TiMBL (Daelemans and Van den Bosch, 2005)) are also popular choices, especially in early applications of machine learning to coreference resolution. In recent years, statistical learners such as maximum entropy models (Berger et al., 1996), voted perceptrons (Freund and Schapire, 1999), and support vector machines (Joachims, 1999) have been increasingly used, in part due to their ability to provide a confidence value (e.g., in the form of a probability) associated with a classification, and in part due to the fact that they can be easily adapted to train recently proposed ranking-based coreference models (...) After training, we can apply the resulting model to a test text, using a clustering algorithm to coordinate the pairwise classification decisions and impose an NP partition.
2008
- (Clark and González-Brenes, 2008) ⇒ Jonathan H. Clark, José P. González-Brenes. (2008). “Coreference: Current Trends and Future Directions." CMU course on Language and Statistics II Literature Review.