DFKI-LT - Semantic Feature Engineering for Enhancing Disambiguation Performance in Deep Linguistic Processing

Danielle Ben-Gera, Yi Zhang, Valia Kordoni
Semantic Feature Engineering for Enhancing Disambiguation Performance in Deep Linguistic Processing
3 Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10), European Language Resources Association (ELRA), 2010
 
The task of parse disambiguation has gained in importance over the last decade as the complexity of grammars used in deep linguistic processing has been increasing. In this paper we propose to employ the fine-grained HPSG formalism in order to investigate the contribution of deeper linguistic knowledge to the task of ranking the different trees the parser outputs. In particular, we focus on the incorporation of semantic features in the disambiguation component and the stability of our model cross domains. Our work is carried out within DELPH-IN (http://www.delph-in.net), using the LinGo Redwoods and the WeScience corpora, parsed with the English Resource Grammar and the PET parser.
 
Files: BibTeX, 494_Paper.pdf, 494_Paper.pdf