Machine Learning
Machine learning for coreference resolution: from local classification to global ranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
An integrated discriminative probabilistic approach to information extraction
Proceedings of the 18th ACM conference on Information and knowledge management
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Coreference resolution is regarded as a crucial step for acquiring linkages among pieces of information extracted. Traditionally, coreference resolution models make use of independent attribute-value features over pairs of noun phrases. However, dependency and deeper relations between features can more adequately describe the properties of coreference relations between noun phrases. In this paper, we propose a framework of coreference resolution based on first-order logic and probabilistic graphical model, the Markov Logic Network. The proposed framework enables the use of background knowledge and captures more complex coreference linkage properties through rich expression of conditions. Moreover, the proposed conditions can capture the structural pattern within a noun phrase as well as contextual information between noun phrases. Our experiments show improvement with the use of the expressive logic models and the use of pattern-based conditions.