1. 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

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab Motivation Natural language is inherently ambiguous. A word can have multiple meanings(or senses).

4. Intelligent Database Systems Lab Objectives This paper evaluates a variety of knowledge sources and supervised learning algorithms for word sense disambiguation on SENSEVAL-2 and SENSEVAL-1 data.

5. Intelligent Database Systems Lab Methodology Part of speech (POS) of Neighboring Words Single Words in the Surrounding Context Local CollocationsSyntactic Relations Knowledge Sources

6. 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. bars and NNP VBD PRP VBGIN DTNNNNS. {IN,DT,NN,NNS,., ,  }

7. Intelligent Database Systems Lab Methodology Single Words in the Surrounding Context – Feature selection method Parameter:M 2 {chocolate, iron, beer} Reid saw me looking at the iron bars. bars

8. Intelligent Database Systems Lab Methodology Local Collocations – This paper extracted 11 features. C -1,-1,C 1,1,C -2,-2,C 2,2,C -2,-1,C -1,1,C 1,2,C -3,-1,C -2,1,C -1,2,C 1,3 { a_chocolate, the_wine, the_iron } Reid saw me looking at the iron bars. bars C -2,-1

9. Intelligent Database Systems Lab 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

10. Intelligent Database Systems Lab Learning Algorithms – Support Vector Machines – AdaBoost – Naïve Bayes – Decision Trees Evaluation Data Sets – SENSEVAL-2 – SENSEVAL-1 Methodology

11. Intelligent Database Systems Lab Experiments

12. Intelligent Database Systems Lab Experiments

13. Intelligent Database Systems Lab Experiments

14. Intelligent Database Systems Lab Experiments

15. 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.

16. Intelligent Database Systems Lab Comments Advantages – This paper easy to read. Applications – WSD