TY - JOUR
T1 - Joint incremental disfluency detection and dependency parsing
AU - Honnibal, Matthew
AU - Johnson, Mark
N1 - Copyright the Publisher 2014. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2014
Y1 - 2014
N2 - We present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. For comparison, we evaluated two pipeline systems, using state-of-the-art disfluency detectors. The joint model performed better on both tasks, with a parse accuracy of 90.5% and 84.0% accuracy at disfluency detection. The model runs in expected linear time, and processes over 550 tokens a second.
AB - We present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. For comparison, we evaluated two pipeline systems, using state-of-the-art disfluency detectors. The joint model performed better on both tasks, with a parse accuracy of 90.5% and 84.0% accuracy at disfluency detection. The model runs in expected linear time, and processes over 550 tokens a second.
UR - http://purl.org/au-research/grants/arc/DP110102506
UR - http://purl.org/au-research/grants/arc/DP110102593
M3 - Article
SN - 2307-387X
VL - 2
SP - 131
EP - 142
JO - Transactions of the Association for Computational Linguistics
JF - Transactions of the Association for Computational Linguistics
IS - 1
ER -