Paper: Joint Training and Decoding Using Virtual Nodes for Cascaded Segmentation and Tagging Tasks

ACL ID D10-1019
Title Joint Training and Decoding Using Virtual Nodes for Cascaded Segmentation and Tagging Tasks
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

Many sequence labeling tasks in NLP require solving a cascade of segmentation and tag- ging subtasks, such as Chinese POS tagging, named entity recognition, and so on. Tradi- tional pipeline approaches usually suffer from error propagation. Joint training/decoding in the cross-product state space could cause too many parameters and high inference complex- ity. In this paper, we present a novel method which integrates graph structures of two sub- tasks into one using virtual nodes, and per- forms joint training and decoding in the fac- torized state space. Experimental evaluations on CoNLL 2000 shallow parsing data set and Fourth SIGHAN Bakeoff CTB POS tagging data set demonstrate the superiority of our method over cross-product, pipeline and can- didate reranking approaches.