One Class per Named Entity: Exploiting Unlabeled Text for Named Entity Recognition

In this paper, we present a simple yet novel method of exploiting unlabeled text to further improve the accuracy of a high-performance state-of-theart named entity recognition (NER) system. The method utilizes the empirical property that many named entities occur in one name class only. Using only unlabeled text as the additional resource, our improved NER system achieves an F1 score of

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Yingchuan Wong, Hwee Tou Ng
• Keywords: Artificial Intelligence, Empirical Property, IJCAI 2007, Named Entity Recognition, Unlabeled Text