An efficient agglomerative clustering algorithm using a heap
Pattern Recognition
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Optimization of relevance feedback weights
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Name disambiguation in author citations using a K-way spectral clustering method
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Comparative study of name disambiguation problem using a scalable blocking-based framework
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
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
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Unsupervised personal name disambiguation
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Web people search: results of the first evaluation and the plan for the second
Proceedings of the 17th international conference on World Wide Web
A study of methods for negative relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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This paper is concerned with the problem of disambiguating Web people search result. Finding the information about people is one of the most common activities on the Web. However, the result of searching person names suffers a lot from the problem of ambiguity. In this paper, we propose a classification framework to solve this problem using an additional feedback page. Compared with the traditional solution which clusters the search result, our framework has lower computational complexity and better effect. we also developed two new features under the framework, which utilized the information beyond tokens. Experiments show that the performance can be improved greatly using the two features. Different classification methods are also compared for their effectiveness for the task.