Clustering Algorithms
Automatic identification of non-compositional phrases
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Unsupervised type and token identification of idiomatic expressions
Computational Linguistics
Unsupervised recognition of literal and non-literal use of idiomatic expressions
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Automatic identification of non-compositional multi-word expressions using latent semantic analysis
MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Classifier combination for contextual idiom detection without labelled data
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Robot Programming by Demonstration
Robot Programming by Demonstration
From humor recognition to irony detection: The figurative language of social media
Data & Knowledge Engineering
Automatic detection of idiomatic clauses
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Evaluating the premises and results of four metaphor identification systems
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Determining the conceptual space of metaphoric expressions
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Towards a model for replicating aesthetic literary appreciation
Proceedings of the Fifth Workshop on Semantic Web Information Management
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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.