A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Handling Missing Values when Applying Classification Models
The Journal of Machine Learning Research
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Bioinformatics
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We consider the problem of predicting which words a student will click in a vocabulary learning system. Often a language learner will find value in the ability to look up the meaning of an unknown word while reading an electronic document by clicking the word. Highlighting words likely to be unknown to a reader is attractive due to drawing his or her attention to it and indicating that information is available. However, this option is usually done manually in vocabulary systems and online encyclopedias such as Wikipedia. Furthurmore, it is never on a per-user basis. This paper presents an automated way of highlighting words likely to be unknown to the specific user. We present related work in search engine ranking, a description of the study used to collect click data, the experiment we performed using the random forest machine learning algorithm and finish with a discussion of future work.