An Alternative to Head-Driven Approaches for Parsing a (Relatively) Free Word-Order Language

Applying statistical parsers developed for English to languages with freer wordorder has turned out to be harder than expected. This paper investigates the adequacy of different statistical parsing models for dealing with a (relatively) free word-order language. We show that the recently proposed RelationalRealizational (RR) model consistently outperforms state-of-the-art Head-Driven (HD) models on the Hebrew Treebank. Our analysis reveals a weakness of HD models: their intrinsic focus on configurational information. We conclude that the form-function separation ingrained in RR models makes them better suited for parsing nonconfigurational phenomena.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Reut Tsarfaty, Khalil Sima'an, Remko Scha
• Keywords: EMNLP 2009, Free Word-order Language, Natural Language Processing, Statistical Parsers, Statistical Parsing Models