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Abstract
Incremental parsers have potential advantages
for applications like language modeling for
machine translation and speech recognition.
We describe a new algorithm for incremental
transition-based Combinatory Categorial
Grammar parsing. As English CCGbank
derivations are mostly right branching and
non-incremental, we design our algorithm
based on the dependencies resolved rather
than the derivation. We introduce two new actions in the shift-reduce paradigm based on the
idea of ‘revealing’ (Pareschi and Steedman,
1987) the required information during parsing. On the standard CCGbank test data, our
algorithm achieved improvements of 0.88%
in labeled and 2.0% in unlabeled F-score over
a greedy non-incremental shift-reduce parser.
Original language | English |
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Title of host publication | Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies |
Place of Publication | Denver, Colorado |
Publisher | Association for Computational Linguistics |
Pages | 53-63 |
Number of pages | 11 |
ISBN (Print) | 978-1-941643-49-5 |
Publication status | Published - 1 May 2015 |
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Dive into the research topics of 'An Incremental Algorithm for Transition-based CCG Parsing'. Together they form a unique fingerprint.Projects
- 2 Finished
-
Xperience - 'Robotes Bootstrapped through Learning from Experience'
Steedman, M., Geib, C. & Petrick, R.
1/01/10 → 31/12/15
Project: Research