Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 131-142 |
| Number of pages | 12 |
| Journal | Transactions of the Association for Computational Linguistics |
| Volume | 2 |
| Issue number | 1 |
| Publication status | Published - 2014 |
Bibliographical note
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.Fingerprint Dive into the research topics of 'Joint incremental disfluency detection and dependency parsing'. Together they form a unique fingerprint.
Cite this
Honnibal, M., & Johnson, M. (2014). Joint incremental disfluency detection and dependency parsing. Transactions of the Association for Computational Linguistics, 2(1), 131-142.