Improving Semantic Frame Accuracy for Semantic Dependency Parsing

110 Pages·2017·2.73 MB·English

Improving Semantic Frame Accuracy
for Semantic Dependency Parsing
Arash Saidi
Thesis submitted for the degree of
Master in Informatics: Language and Communication
60 credits
Faculty of mathematics and natural sciences
UNIVERSITY OF OSLO
Spring 2017

Improving Semantic Frame Accuracy
for Semantic Dependency Parsing
Arash Saidi

(cid:13)c 2017ArashSaidi
ImprovingSemanticFrameAccuracyforSemanticDependencyParsing
http://www.duo.uio.no/
Printed: Reprosentralen,UniversityofOslo
Abstract
This thesis presents an in-depth contrastive error analysis of a set of semantic
dependency parsing systems. Based on the empirical results of our analysis we
foundsemanticframeclassificationtobeaninterestingcasestudy. Aspartofthis
thesiswehavemadeasemanticframeclassifierthatoutperformspreviousresults.
The semantic frame classifier is the result of rigorous experimentation with four
setoffeatures: (1)lexical,(2)morphological,(3)syntactic,and(4)semantic. We
showthatourresultsoutperformpreviousresults. Wealsoshowthatourclassifier
can be used to extend and improve the frame semantic classification accuracy of
twoexistingstate-of-the-artsemanticdependencyparsingsystems.

Acknowledgements
This thesis is submitted for the degree of Master of Science at the University of
Oslo. My supervisors have been Stephan Oepen and Lilja Øvrelid. I am thankful
fortheirinsights,helpfuladviceandpatience.
I want to thank my good friend, fellow student, and co-worker Petter Hohle
forhisencouragement. IthankmygirlfriendMaritforeverything.

Contents
Contents i
ListofTables v
ListofFigures vii
1 Introduction 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Background 5
2.1 DependencyGrammar . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 DefiningDependencies . . . . . . . . . . . . . . . . . . . 7
2.1.2 CriteriaforDependencies . . . . . . . . . . . . . . . . . 8
2.2 DependencyParsing . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 Grammar-DrivenApproaches . . . . . . . . . . . . . . . 11
2.2.2 Data-DrivenApproaches . . . . . . . . . . . . . . . . . . 12
2.3 FromSyntactictoSemanticParsing . . . . . . . . . . . . . . . . 15
2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 SemanticDependencyParsingwithFrames 19
3.1 TargetRepresentations . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.1 Semanticframes . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.1 QuantitativeAnalysisofDataSets . . . . . . . . . . . . . 25
3.3 SubmissionsandTeams . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.1 Peking . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.2 Riga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.3 Turku . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.4 Lisbon . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
i