Handbook of formal languages, vol. 3
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Parsing with Principles and Classes of Information
Parsing with Principles and Classes of Information
Deterministic Techniques for Efficient Non-Deterministic Parsers
Proceedings of the 2nd Colloquium on Automata, Languages and Programming
Mathematical and computational aspects of lexicalized grammars
Mathematical and computational aspects of lexicalized grammars
Lr parsing for tree adjoining grammars and its application to corpus-based natural language parsing
Lr parsing for tree adjoining grammars and its application to corpus-based natural language parsing
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
An alternative LR algorithm for TAGS
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Sentence disambiguation by a shift-reduce parsing technique
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Deterministic left to right parsing of Tree Adjoining Languages
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Fast LR parsing using rich (Tree Adjoining) Grammars
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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In this chapter we discuss practical LR-like parser generator models for Tree Adjoining Grammars (TAGs) and propose a new algorithm. The algorithm has been implemented and applied to two large coverage TAGs for English: the XTAG English grammar, and a grammar automatically extracted from the Penn Treebank. The generated tables have very favorable characteristics compared to an existing approach by Nederhof, undermining earlier beliefs that LR parsing for TAGs would be inadequate for parsing natural language. Indeed, our parser generator has been used to build fast accurate best-parse parsers for natural language, as reported in (Prolo, 2002a).