Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Information Extraction Tools: Deciphering Human Language
IT Professional
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The SemEval-2007 WePS evaluation: establishing a benchmark for the web people search task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Efficient name disambiguation for large-scale databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
| Hi-index | 0.00 |
Cross Document Coreference (CDC) is the problem of resolving the underlying identity of entities across multiple documents and is a major step for document understanding. We develop a framework to efficiently determine the identity of a person based on extracted information, which includes unary properties such as gender and title, as well as binary relationships with other named entities such as co-occurrence and geo-locations. At the heart of our approach is a suite of similarity functions (specialists) for matching relationships and a relational density-based clustering algorithm that delineates name clusters based on pairwise similarity. We demonstrate the effectiveness of our methods on the WePS benchmark datasets and point out future research directions.