Paper: A Fast Decoder for Joint Word Segmentation and POS-Tagging Using a Single Discriminative Model

ACL ID D10-1082
Title A Fast Decoder for Joint Word Segmentation and POS-Tagging Using a Single Discriminative Model
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

We show that the standard beam-search al- gorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-tagging, achieving a significant speed im- provement. Such decoding is enabled by: (1) separating full word features from par- tial word features so that feature templates can be instantiated incrementally, according to whether the current character is separated or appended; (2) deciding the POS-tag of a poten- tial word when its first character is processed. Early-update is used with perceptron training so that the linear model gives a high score to a correct partial candidate as well as a full out- put. Effective scoring of partial structures al- lows the decoder to give high accuracy with a small beam-size of 16. ...