Joint Segmentation and Tagging with Coupled Sequences Labeling
Xipeng Qiu, Feng Quan Ji, Jiayi Zhao, Xuanjing Huang · 2012
Segmentation and tagging task is the fundamental problem in natural language processing (NLP). Traditional methods solve this problem in either pipeline or joint cross-label ways, which suffer from error propagation and large number of labels respectively. In this paper, we present a novel joint model for segmentation and tagging, which integrates two dependent Markov chains. One chain is used for segmentation, and the other is for tagging. The model parameters can be estimated simultaneously. Besides, we can optimize the whole model by improving the single chain. The experiments show that our model could achieve higher performance over traditional models on both English shallow parsing and Chinese word segmentation and POS tagging tasks. T��� � �� � A������ � � � C������