Machine Learning
WordNet: a lexical database for English
Communications of the ACM
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Links between perceptrons, MLPs and SVMs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Kernel methods for minimally supervised wsd
Computational Linguistics
Using wiktionary to improve lexical disambiguation in multiple languages
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Semantic relatedness for biomedical word sense disambiguation
TextGraphs-7 '12 Workshop Proceedings of TextGraphs-7 on Graph-based Methods for Natural Language Processing
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Word Sense Disambiguation (WSD) is an AI-complete problem where senses of words in the documents must be correctly selected from a senses inventory. Support Vector Machines (SVM) method has been successfully applied to supervised WSD. In contrast, perceptron has not been popular in supervised WSD. In this paper, a supervised method combining Margin Perceptron (MP) and Platt's probabilistic output is proposed to solve the word sense ambiguity problem. Experiments were conducted on Senseval-3 English Lexical Sample Task data set. The performance is comparable with systems using SVMs. Our system is in line with the best system participating in Senseval-3, regarding that we only used given training data, and no classifiers combination technique was applied. The advantage of our method is mainly two-fold: Firstly, good achieved performance shows that MP can be applied to problem with limited training data, especially in natural language processing. Secondly, MP algorithm used in this work is easy to implement, which benefits the application and the extension of the algorithm.