LR parsers for natural languages Export

@inproceedings{Masaru:1984:GLR, abstract = {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.}, address = {Morristown, NJ, USA}, author = {Tomita, Masaru}, booktitle = {ACL-22: Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics}, citeulike-article-id = {5914351}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=980564}, citeulike-linkout-1 = {http://dx.doi.org/10.3115/980431.980564}, doi = {10.3115/980431.980564}, keywords = {dynamic\_programming, glr, lr\_parser, natural\_languages}, location = {Stanford, California}, pages = {354--357}, posted-at = {2009-11-04 01:54:49}, priority = {2}, publisher = {Association for Computational Linguistics}, title = {LR parsers for natural languages}, url = {http://dx.doi.org/10.3115/980431.980564}, year = {1984} }

TY - CONF ID - Masaru:1984:GLR L3 - citeulike-article-id:5914351 N2 - 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. CY - Stanford, California AD - Morristown, NJ, USA EP - 357 TI - LR parsers for natural languages PB - Association for Computational Linguistics T2 - ACL-22: Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics SP - 354 KW - dynamic_programming KW - glr KW - lr_parser KW - natural_languages AU - Tomita, Masaru PY - 1984/// UR - http://dx.doi.org/10.3115/980431.980564 ER -