Semantic role labeling using rich morphological features
Lundgren, David
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https://hdl.handle.net/2142/44091
Description
Title
Semantic role labeling using rich morphological features
Author(s)
Lundgren, David
Issue Date
2013-05-24T21:50:24Z
Director of Research (if dissertation) or Advisor (if thesis)
Girju, Roxana
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2013-05-24T21:50:24Z
Keyword(s)
semantic role labeling (SRL)
Arabic
morphology
semantics
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.
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