An efficient augmented-context-free parsing algorithm
Computational Linguistics
A maximum entropy approach to natural language processing
Computational Linguistics
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
The generalized LR parser/compiler V8-4: a software package for practical NL projects
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 1
An efficient implementation of a new DOP model
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Deterministic dependency parsing of English text
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
A classifier-based parser with linear run-time complexity
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Measuring efficiency in high-accuracy, broad-coverage statistical parsing
Proceedings of the COLING-2000 Workshop on Efficiency In Large-Scale Parsing Systems
Algorithms for deterministic incremental dependency parsing
Computational Linguistics
Incremental parsing models for dialog task structure
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Deterministic shift-reduce parsing for unification-based grammars by using default unification
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Fast full parsing by linear-chain conditional random fields
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Nbest dependency parsing with linguistically rich models
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Evaluating the effects of treebank size in a practical application for parsing
SETQA-NLP '08 Software Engineering, Testing, and Quality Assurance for Natural Language Processing
High-accuracy annotation and parsing of CHILDES transcripts
CACLA '07 Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition
Bilingually-constrained (monolingual) shift-reduce parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
An efficient algorithm for easy-first non-directional dependency parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A data-driven case-based reasoning approach to interactive storytelling
ICIDS'10 Proceedings of the Third joint conference on Interactive digital storytelling
Incremental combinatory categorial grammar and its derivations
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Better automatic treebank conversion using a feature-based approach
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
A machine learning parser using an unlexicalized distituent model
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Thinking outside the box for natural language processing
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Say Anything: Using Textual Case-Based Reasoning to Enable Open-Domain Interactive Storytelling
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Common Sense for Interactive Systems
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
A feature-based approach to better automatic treebank conversion
Language Resources and Evaluation
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Recently proposed deterministic classifier-based parsers (Nivre and Scholz, 2004; Sagae and Lavie, 2005; Yamada and Mat-sumoto, 2003) offer attractive alternatives to generative statistical parsers. Deterministic parsers are fast, efficient, and simple to implement, but generally less accurate than optimal (or nearly optimal) statistical parsers. We present a statistical shift-reduce parser that bridges the gap between deterministic and probabilistic parsers. The parsing model is essentially the same as one previously used for deterministic parsing, but the parser performs a best-first search instead of a greedy search. Using the standard sections of the WSJ corpus of the Penn Treebank for training and testing, our parser has 88.1% precision and 87.8% recall (using automatically assigned part-of-speech tags). Perhaps more interestingly, the parsing model is significantly different from the generative models used by other well-known accurate parsers, allowing for a simple combination that produces precision and recall of 90.9% and 90.7%, respectively.