The syntactic process
A best-first probabilistic shift-reduce parser
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
Algorithms for deterministic incremental dependency parsing
Computational Linguistics
Toward a psycholinguistically-motivated model of language processing
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Incrementality in deterministic dependency parsing
IncrementParsing '04 Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
Positive results for parsing with a bounded stack using a model-based right-corner transform
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A syntactified direct translation model with linear-time decoding
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
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Incremental parsing is appealing for applications such as speech recognition and machine translation due to its inherent efficiency as well as being a natural match for the language models commonly used in such systems. In this paper we introduce an Incremental Combinatory Categorical Grammar (ICCG) that extends the standard CCG grammar to enable fully incremental left-to-right parsing. Furthermore, we introduce a novel dynamic programming algorithm to convert CCGbank normal form derivations to incremental left-to-right derivations and show that our incremental CCG derivations can recover the unlabeled predicate-argument dependency structures with more than 96% F-measure. The introduced CCG incremental derivations can be used to train an incremental CCG parser.