Hautli, Annette, Cetinoglu, Ozlem and van Genabith, Josef ORCID: 0000-0003-1322-7944 (2010) Closing the gap between stochastic and rule-based LFG grammars. In: the LFG10 Conference, 18-20 July 2010, Ottowa, Canada.
Abstract
Developing large-scale deep grammars in a constraint-based framework such as Lexical Functional Grammar (LFG) is time-consuming and requires significant linguistic insight. Recently, treebank-based constraint-grammar acquisition
approaches have been developed as an alternative to hand-crafting such resources. While treebank-based approaches are wide coverage and robust and achieve competitive evaluation results for many languages, the granularity of the linguistic analyses provided by treebank-based resources
tends to be less fine-grained than what is offered by state-of-the-art handcrafted grammars. This paper presents an approach to extend the English DCU LFG annotation algorithm with more detailed f-structure information to provide probabilistic treebank-based LFG grammars with rich feature information comparable to that implemented by the hand-crafted English XLE grammar, while maintaining the robustness and the coverage of treebankbased stochastic grammars.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Event Type: | Conference |
Refereed: | Yes |
Uncontrolled Keywords: | Lexical Functional Grammar; LFG |
Subjects: | Computer Science > Machine translating |
DCU Faculties and Centres: | Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL) Research Initiatives and Centres > National Centre for Language Technology (NCLT) |
Published in: | Proceedings of LFG10. . CSLI Publications. |
Publisher: | CSLI Publications |
Official URL: | http://cslipublications.stanford.edu/LFG/15/abstra... |
Copyright Information: | © 2010 CSLI Publications |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
ID Code: | 16017 |
Deposited On: | 19 May 2011 09:40 by Shane Harper . Last Modified 20 Jan 2022 16:04 |
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