Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Hunting elusive metaphors using lexical resources
FigLanguages '07 Proceedings of the Workshop on Computational Approaches to Figurative Language
ScaNaLU '06 Proceedings of the Third Workshop on Scalable Natural Language Understanding
Topic models for word sense disambiguation and token-based idiom detection
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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Psycholinguistic studies of metaphor processing must control their stimuli not just for word frequency but also for the frequency with which a term is used metaphorically. Thus, we consider the task of metaphor frequency estimation, which predicts how often target words will be used metaphorically. We develop metaphor classifiers which represent metaphorical domains through Latent Dirichlet Allocation, and apply these classifiers to the target words, aggregating their decisions to estimate the metaphorical frequencies. Training on only 400 sentences, our models are able to achieve 61.3% accuracy on metaphor classification and 77.8% accuracy on High vs. Low metaphorical frequency estimation.