Year: 2010
Type: conference-paper
Abstract: We show that naïve modeling of morphosyn-tactic agreement in a Constituency-Based (CB) statistical parsing model is worse than none, whereas a linguistically adequate way of modeling inflectional morphology in CB parsing leads to improved performance. In particular, we show that an extension of the Relational-Realizational (RR) model that incorporates agreement features is superior to CB models that treat morphosyntax as state-splits (SP), and that the RR model benefits more from inflectional features. We focus on parsing Hebrew and report the best result to date, F184.13 for parsing off of gold-tagged text, 5% error reduction from previous results.
Authors: Tsarfaty, R., Sima'an, K.
Institutions: Uppsala University, University of Amsterdam
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2142220626