A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Is it harder to parse Chinese, or the Chinese Treebank?
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Joint unsupervised coreference resolution with Markov logic
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Coreference resolution in a modular, entity-centered model
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A multi-pass sieve for coreference resolution
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Bootstrapping coreference resolution using word associations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Coreference resolution with world knowledge
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Stanford's multi-pass sieve coreference resolution system at the CoNLL-2011 shared task
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Deterministic coreference resolution based on entity-centric, precision-ranked rules
Computational Linguistics
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We in this paper present the model for our participation (BCMI) in the CoNLL-2012 Shared Task. This paper describes a pure rule-based method, which assembles different filters in a proper order. Different filters handle different situations and the filtering strategies are designed manually. These filters are assigned to different ordered tiers from general to special cases. We participated in the Chinese and English closed tracks, scored 51.83 and 59.24 respectively.