Tagging complex NEs with maxent models: layered structures versus extended tagset

Authors:
Deyi Xiong;Hongkui Yu;Qun Liu
Affiliations:
Institute of Computing Technology, the Chinese Academy of Sciences, Beijing;Institute of Computing Technology, the Chinese Academy of Sciences, Beijing;Institute of Computing Technology, the Chinese Academy of Sciences, Beijing
Venue:
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Year:
2004

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Abstract

The paper discusses two policies for recognizing NEs with complex structures by maximum entropy models. One policy is to develop cascaded MaxEnt models at different levels. The other is to design more detailed tags with human knowledge in order to represent complex structures. The experiments on Chinese organization names recognition indicate that layered structures result in more accurate models while extended tags can not lead to positive results as expected. We empirically prove that the {start, continue, end, unique, other} tag set is the best tag set for NE recognition with MaxEnt models.