Abstract
In this paper a different machine learning approach is presented to deal with the coreference resolution task. This approach consists of a multi-classifier system that classifies mention-pairs in a reduced dimensional vector space. The vector representation for mention-pairs is generated using a rich set of linguistic features. The SVD technique is used to generate the reduced dimensional vector space. The approach is applied to the OntoNotes v4.0 Release Corpus for the column-format files used in CONLL-2011 coreference resolution shared task. The results obtained show that the reduced dimensional representation obtained by SVD is very adequate to appropriately classify mention-pair vectors. Moreover, we can state that the multi-classifier plays an important role in improving the results.
Cite
CITATION STYLE
Zelaia, A., Arregi, O., & Sierra, B. (2015). A multi-classifier approach to support coreference resolution in a vector space model. In 1st Workshop on Vector Space Modeling for Natural Language Processing, VS 2015 at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 (pp. 17–24). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w15-1503
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