Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Whither written language evaluation?
HLT '94 Proceedings of the workshop on Human Language Technology
A testbed for people searching strategies in the WWW
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Name Disambiguation in Person Information Mining
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Using Cross-Document Random Walks for Topic-Focused Multi-Document
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Efficient topic-based unsupervised name disambiguation
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
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
Shallow semantics for coreference resolution
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Name discrimination by clustering similar contexts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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In this paper we present a system for the Web People Search task, which is the task of clustering together the pages referring to the same person. The vector space model approached is modified in order to develop a more flexible clustering technique. We have implemented a dynamic weighting procedure for the attributes common to different cluster in order to maximize the between cluster variance with respect with the within cluster variance. We show that in this way the undesired collateral effect such as superposition and masking are alleviated. The system we present obtains similar results to the ones reported by the top three systems presented at the SEMEVAL 2007 competition.