The nature of statistical learning theory
The nature of statistical learning theory
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine Learning
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Word sense ambiguation: clustering related senses
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Self-assessment of motivation: explicit and implicit indicators in L2 vocabulary learning
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
P-AWL: academic word list for Portuguese
PROPOR'10 Proceedings of the 9th international conference on Computational Processing of the Portuguese Language
Sense-specific lexical information for reading assistance
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Self-Assessment in the REAP Tutor: Knowledge, Interest, Motivation, & Learning
International Journal of Artificial Intelligence in Education
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Words with multiple meanings are a phenomenon inherent to any natural language. In this work, we study the effects of such lexical ambiguities on second language vocabulary learning. We demonstrate that machine learning algorithms for word sense disambiguation can induce classifiers that exhibit high accuracy at the task of disambiguating homonyms (words with multiple distinct meanings). Results from a user study that compared two versions of a vocabulary tutoring system, one that applied word sense disambiguation to support learning and another that did not, support rejection of the null hypothesis that learning outcomes with and without word sense disambiguation are equivalent, with a p-value of 0.001. To our knowledge this is the first work that investigates the efficacy of word sense disambiguation for facilitating second language vocabulary learning.