«Traditionally, unification grammars are hand-coded. This is extremely time consuming, expensive and very difficult to scale. [...] we have developed a new method for automatically extracting wide-coverage probabilistic unification (LFG) grammars from treebank resources. To achieve this, we first automatically annotate the treebank (such as Penn-II) with feature-structure information (LFG f-structures, approximating to basic predicate-argument structure). From the f-structure annotated treebank, we then automatically extract wide-coverage, probabilistic LFG approximations to parse new text»
«takes as input a sequence of phrase-structure trees and modifies their labels according to a set of rules. ... Its rule notation is flexible enough to emulate head/argument-finding rules»
P. Fung, Z. Wu, Y. Yang, and D. Wu. TMI-2007: Proceedings of the 11 th International Conference on Theoretical and Methodological Issues in Machine Translation, page 75--84. Skövde, Sweden, (2007)
R. Hwa, P. Resnik, A. Weinberg, and O. Kolak. ACL '02: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, page 392--399. Morristown, NJ, USA, Association for Computational Linguistics, (2001)
L. Karttunen, R. Kaplan, and A. Zaenen. Proceedings of the 14th conference on Computational linguistics, page 141--148. Morristown, NJ, USA, Association for Computational Linguistics, (1992)
C. Callison-Burch, M. Osborne, and P. Koehn. Proceedings the Eleventh Conference of the European Chapter of the Association for Computational Linguistics, page 249--256. Trento, Italia, (2006)
S. Riezler, J. Kuhn, D. Prescher, and M. Johnson. ACL '00: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, page 480--487. Morristown, NJ, USA, Association for Computational Linguistics, (2000)