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Title:Semantic role labeling using rich morphological features
Author(s):Lundgren, David
Advisor(s):Girju, Roxana
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):semantic role labeling (SRL)
Abstract:In this thesis, I examine the impact of morphological features on semantic role labeling (SRL) in Modern Standard Arabic. This study provides the first rigorous examination of the effect of individual morphological attributes on the automatic classification of predicate-argument roles in Arabic. I obtain a classification accuracy comparable to the best-published previous results using fewer features and a linear kernel SVM. This provides evidence that there is a strong morpho-semantic interaction in Arabic and that careful, language-dependent feature engineering may provide substantial improvements to morphologically rich languages. I demonstrate that the incorporation of rich-morphological features substantially outperform the established lexico-syntactic featuresets of standard SRL systems. A principled feature selection algorithm for SRL is also introduced that significantly decreases generalization error, leading to state-of-the-art argument role classification results.
Issue Date:2013-05-24
Rights Information:Copyright 2013 David Lundgren
Date Available in IDEALS:2013-05-24
Date Deposited:2013-05

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