Presenter : Kung, Chien-Hao Authors : Yoong Keok Lee and Hwee Tou Ng 2002,EMNLP
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Transcript of Presenter : Kung, Chien-Hao Authors : Yoong Keok Lee and Hwee Tou Ng 2002,EMNLP
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Intelligent Database Systems Lab
Presenter : Kung, Chien-Hao
Authors : Yoong Keok Lee and Hwee Tou Ng
2002,EMNLP
An Empirical Evaluation of Knowledge Sources and learining Algorithms for
Word Sense Disambiguation
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Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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Intelligent Database Systems Lab
Motivation• Natural language is inherently ambiguous.
• A word can have multiple meanings(or senses).
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Objectives• This paper evaluates a variety of knowledge sources
and supervised learning algorithms for word sense
disambiguation on SENSEVAL-2 and SENSEVAL-1 data.
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Methodology
Part of speech (POS) of Neighboring Words
Single Words in the Surrounding Context
Local CollocationsSyntactic Relations
Knowledge Sources
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Intelligent Database Systems Lab
Methodology• Part-of-Speech(POS) of Neighboring Words– This paper use 7 features to encode this knowledge source – Setence segmentation program
(Reynar and Ratnaparkhi , 1997)– POS tagger
(Ratnaparkhi , 1996)
Reid saw me looking at the iron bars. barsand
NNP VBD PRP VBG IN DT NN NNS .
{IN,DT,NN,NNS,.,,}
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Intelligent Database Systems Lab
Methodology• Single Words in the Surrounding Context– Feature selection method• Parameter:M2
{chocolate, iron, beer}
Reid saw me looking at the iron bars.
bars
<0,1,0>
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Intelligent Database Systems Lab
Methodology• Local Collocations– This paper extracted 11 features.
C-1,-1 ,C1,1,C-2,-2,C2,2,C-2,-1,C-1,1,C1,2,C-3,-1,C-2,1,C-1,2,C1,3
{ a_chocolate , the_wine , the_iron }
Reid saw me looking at the iron bars.
bars
<the_iron>C-2,-1
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Methodology• Syntactic Relations
(a) Show w and its POS(b) Show the sentence where w occurs(c) Show the feature vector corresponding to syntactic relations
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Intelligent Database Systems Lab
• Learning Algorithms– Support Vector Machines– AdaBoost– Naïve Bayes– Decision Trees
• Evaluation Data Sets– SENSEVAL-2– SENSEVAL-1
Methodology
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Intelligent Database Systems Lab
Experiments
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Intelligent Database Systems Lab
Experiments
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Intelligent Database Systems Lab
Experiments
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Intelligent Database Systems Lab
Experiments
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Intelligent Database Systems Lab
Conclusions• Using all of these knowledge sources and SVM
achieves accuracy higher than the best official scores
on both SENSEVAL-2 and SENSEVAL-a test data.
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Intelligent Database Systems Lab
Comments• Advantages– This paper easy to read.
• Applications– WSD