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Bigrams and BiLSTMs Two neural networks for sequential metaphor detection

Conference paper
Authors Yuri Bizzoni
Mehdi Ghanimifard
Published in Proceedings of the Workshop on Figurative Language Processing at NAACL HLT 2018. 6 June 2018 New Orleans, Louisiana
ISBN 978-1-948087-15-5
Publisher Association of Computational Linguistics (ACL)
Place of publication New Orleans, Louisiana, USA
Publication year 2018
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links https://doi.org/10.18653/v1/W18-091...
Keywords language modeling, metaphor detection, recurrent neural networks
Subject categories Computational linguistics

Abstract

We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feedforward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance.

Page Manager: Webmaster|Last update: 9/11/2012
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