Using Gaussian Mixture Models to Detect Figurative Language in Context

We present a Gaussian Mixture model for detecting different types of figurative language in context. We show that this model performs well when the parameters are estimated in an unsupervised fashion using EM. Performance can be improved further by estimating the parameters from a small annotated data set.

• Type: Journal
• Year: 2010
• Where: NAACL
• Authors: Linlin Li, Caroline Sporleder
• Keywords: Computational Linguistics, Gaussian Mixture Model, NAACL 2010, Small Annotated Data, Unsupervised Fashion