The Journal of Machine Learning Research
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A computer-aided environment for generating multiple-choice test items
Natural Language Engineering
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
SemEval-2007 task 07: coarse-grained English all-words task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
On the use of automatically acquired examples for all-nouns word sense disambiguation
Journal of Artificial Intelligence Research
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Extracting glosses to disambiguate word senses
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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Learning a vocabulary word requires seeing it in multiple informative contexts. We describe a system to generate such contexts for a given word sense. Rather than attempt to do word sense disambiguation on example contexts already generated or selected from a corpus, we compile information about the word sense into the context generation process. To evaluate the sense-appropriateness of the generated contexts compared to WordNet examples, three human judges chose which word sense(s) fit each example, blind to its source and intended sense. On average, one judge rated the generated examples as sense-appropriate, compared to two judges for the WordNet examples. Although the system's precision was only half of WordNet's, its recall was actually higher than WordNet's, thanks to covering many senses for which WordNet lacks examples.