Paper Info
Title
Using Gaussian Mixture models to detect figurative language in context
Abstract
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.
Year
Venue
Keywords
2010
HLT-NAACL
unsupervised fashion,small annotated data,different type,figurative language,gaussian mixture model
Field
DocType
ISBN
Computer science,Speech recognition,Artificial intelligence,Natural language processing,Literal and figurative language,Mixture model,Machine learning
Conference
1-932432-65-5
Citations 
PageRank 
References 
17
0.93
5
Authors
2
Name
Order
Citations
PageRank
Linlin Li11177.66
Caroline Sporleder245331.84