Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Acquisition of categorized named entities for web search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Named entity recognition with a maximum entropy approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Semantic annotation, indexing, and retrieval
Web Semantics: Science, Services and Agents on the World Wide Web
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Named entities play an important role in many Natural Language Processing applications. Currently, most named entity recognition systems rely on a small set of general named entity (NE) types. Though some efforts have been proposed to expand the hierarchy of NE types, there are still a fixed number of NE types. In real applications, such as question answering or semantic search systems, users may be interested in more diverse specific NE types. This paper proposes a method to extract categories of person named entities from text documents. Based on Dual Iterative Pattern Relation Extraction method, we develop a more suitable model for solving our problem, and explore the generation of different pattern types. A method for validating whether a category is valid or not is proposed to improve the performance, and experiments on Wall Street Journal corpus give promising results.