Tagging Complex NEs with MaxEnt Models: Layered Structures Versus Extended Tagset

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.

• Type: Conference
• Year: 2004
• Where: IJCNLP
• Authors: Deyi Xiong, Hongkui Yu, Qun Liu
• Keywords: Complex Structures, IJCNLP 2004, MaxEnt Models, Maximum Entropy Models