Multilevel coarse-to-fine PCFG parsing
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Lattice parsing to integrate speech recognition and rule-based machine translation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Language-independent parsing with empty elements
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Fast syntactic analysis for statistical language modeling via substructure sharing and uptraining
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Large-scale syntactic language modeling with treelets
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Word segmentation, unknown-word resolution, and morphological agreement in a hebrew parsing system
Computational Linguistics
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This thesis explores a language modeling technique based on statistical parsing. Previous research that exploits syntactic structure for modeling language has shown improved accuracy over the standard trigram models. Unlike previous techniques, our parsing model performs syntactic analysis on sets of hypothesized word-strings simultaneously; these sets are encoded as weighted finite state automata word-lattices. We present a best-first word-lattice chart parsing algorithm which combines the search for good parses with the search for good strings in the word-lattice. We describe how the word-lattice parser is combined with the Charniak language model, a sophisticated syntactic language model, in order to provide an efficient syntactic language model. We present results for this model on a standard set of speech recognition word-lattices. Finally, we examine variations of the word-lattice parser in order to increase performance as well as accuracy.