Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A decision-based approach to rhetorical parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Deterministic dependency parsing of English text
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
A latent variable model of synchronous parsing for syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Shift-reduce dependency DAG parsing
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
Sentence boundary detection and the problem with the U.S.
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
A classifier-based parser with linear run-time complexity
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Open-domain commonsense reasoning using discourse relations from a corpus of weblog stories
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Semi-supervised discourse relation classification with structural learning
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Evaluating temporal graphs built from texts via transitive reduction
Journal of Artificial Intelligence Research
Predicting thread discourse structure over technical web forums
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SemEval-2012 task 7: choice of plausible alternatives: an evaluation of commonsense causal reasoning
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Text-level discourse parsing with rich linguistic features
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Discourse structure and language technology
Natural Language Engineering
Semantic role labeling of implicit arguments for nominal predicates
Computational Linguistics
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We present an efficient approach for discourse parsing within and across sentences, where the unit of processing is an entire document, and not a single sentence. We apply shift-reduce algorithms for dependency and constituent parsing to determine syntactic dependencies for the sentences in a document, and subsequently a Rhetorical Structure Theory (RST) tree for the entire document. Our results show that our linear-time shift-reduce framework achieves high accuracy and a large improvement in efficiency compared to a state-of-the-art approach based on chart parsing with dynamic programming.