Metaphor is one of the most studied and widespread figures of speech and an essential element of individual style. In this paper we look at metaphor identification in Adjective-Noun pairs. We show that using a single neural network combined with pre-trained vector embeddings can outperform the state of the art in terms of accuracy. In specific, the approach presented in this paper is based on two ideas: a) transfer learning via using pre-trained vectors representing adjective noun pairs, and b) a neural network as a model of composition that predicts a metaphoricity score as output. We present several different architectures for our system and evaluate their performances. Variations on dataset size and on the kinds of embeddings are also investigated. We show considerable improvement over the previous approaches both in terms of accuracy and w.r.t the size of annotated training data.
In REF , pre-trained noun and adjective vector embeddings are fed to a single-layered neural network to disambiguate metaphorical and literal
"Deep" Learning : Detecting Metaphoricity in Adjective-Noun Pairs
Proceedings of the Workshop on Stylistic Variation
Proceedings of the Workshop on Stylistic Variation
Computer Science