ACM Computing Surveys (CSUR)
Deterministic parsing of ambiguous grammars
Communications of the ACM
Communications of the ACM
The Theory of Parsing, Translation, and Compiling
The Theory of Parsing, Translation, and Compiling
Principles of Compiler Design (Addison-Wesley series in computer science and information processing)
Principles of Compiler Design (Addison-Wesley series in computer science and information processing)
An efficient augmented-context-free parsing algorithm
Computational Linguistics
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
An efficient context-free parsing algorithm for natural languages
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
The third rewrite engines competition
WRLA'10 Proceedings of the 8th international conference on Rewriting logic and its applications
Parsing with derivatives: a functional pearl
Proceedings of the 16th ACM SIGPLAN international conference on Functional programming
Enforcing security with behavioral fingerprinting
Proceedings of the 7th International Conference on Network and Services Management
Scalable analysis of variable software
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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MLR, an extended LR parser, is introduced, and its application to natural language parsing is discussed. An LR parser is a shift-reduce parser which is deterministically guided by a parsing table. A parsing table can be obtained automatically from a context-free phrase structure grammar. LR parsers cannot manage ambiguous grammars such as natural language grammars, because their parsing tables would have multiply-defined entries, which precludes deterministic parsing. MLR, however, can handle multiply-defined entries, using a dynamic programming method. When an input sentence is ambiguous, the MLR parser produces all possible parse trees without parsing any part of the input sentence more than once in the same way, despite the fact that the parser does not maintain a chart as in chart parsing. Our method also provides an elegant solution to the problem of multi-part-of-speech words such as "that". The MLR parser and its parsing table generator have been implemented at Carnegie-Mellon University.