The current study investigates approaches to automatic metaphor identification, the computational task of identifying whether a word or phrase in a portion of text is an instance of metaphor. In addition to using the Skip-Gram and Continuous Bag-of-Words algorithms for word-level feature extraction, the Paragraph Vector is utilized for obtaining sentence-level distributional information, being an extension to these two algorithms for blocks of text larger than the word level. With features extracted using the above models, the performance of several different neural network systems are compared against a baseline of logistic regression on the VU Amsterdam Metaphor Corpus, with results showing a significant improvement and high success rates across the different models. This can be seen as strong evidence for the necessity of using state-of-the-art neural network architectures in supervised metaphor identification, being able to pick up on the various latent patterns provided by the vector space model.
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