%0 Conference Proceedings %T A New Formalization of Probabilistic GLR Parsing %A Unui, Kentaro %A Sornlertlamvanich, Virach %A Tanaka, Hozumi %A Tokunaga, Takenobu %Y Nijholt, Anton %Y Berwick, Robert C. %Y Bunt, Harry C. %Y Carpenter, Bob %Y Hajicova, Eva %Y Johnson, Mark %Y Joshi, Aravind %Y Kaplan, Ronald %Y Kay, Martin %Y Lang, Bernard %Y Lavie, Alon %Y Nagao, Makoto %Y Steedman, Mark %Y Tomita, Masaru %Y Vijay-Shanker, K. %Y Weir, David %Y Wittenburg, Kent %Y Wiren, Mats %S Proceedings of the Fifth International Workshop on Parsing Technologies %D 1997 %8 sep 17 20 %I Association for Computational Linguistics %C Boston/Cambridge, Massachusetts, USA %F unui-etal-1997-new %X This paper presents a new formalization of probabilistic GLR language modeling for statistical parsing. Our model inherits its essential features from Briscoe and Carroll’s generalized probabilistic LR model, which obtains context-sensitivity by assigning a probability to each LR parsing action according to its left and right context. Briscoe and Carroll’s model, however, has a drawback in that it is not formalized in any probabilistically well-founded way, which may degrade its parsing performance. Our formulation overcomes this drawback with a few significant refinements, while maintaining all the advantages of Briscoe and Carroll’s modeling. %U https://aclanthology.org/1997.iwpt-1.16 %P 123-134