The interface between phrasal and functional constraints
Computational Linguistics
Supertagging: an approach to almost parsing
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
COLING-GEE '02 Proceedings of the 2002 workshop on Grammar engineering and evaluation - Volume 15
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The importance of supertagging for wide-coverage CCG parsing
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Design of a multi-lingual, parallel-processing statistical parsing engine
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Extremely lexicalized models for accurate and fast HPSG parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Efficient HPSG parsing with supertagging and CFG-filtering
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Speeding up LFG parsing using c-structure pruning
GEAF '08 Proceedings of the Workshop on Grammar Engineering Across Frameworks
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The demand for deep linguistic analysis for huge volumes of data means that it is increasingly important that the time taken to parse such data is minimized. In the XLE parsing model which is a hand-crafted, unification-based parsing system, most of the time is spent on unification, searching for valid f-structures (dependency attribute-value matrices) within the space of the many valid c-structures (phrase structure trees). We carried out an experiment to determine whether pruning the search space at an earlier stage of the parsing process results in an improvement in the overall time taken to parse, while maintaining the quality of the f-structures produced. We retrained a state-of-the-art probabilistic parser and used it to pre-bracket input to the XLE, constraining the valid c-structure space for each sentence. We evaluated against the PARC 700 Dependency Bank and show that it is possible to decrease the time taken to parse by ~18% while maintaining accuracy.