@inproceedings{zayed-etal-2018-phrase,
title = "Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning",
author = "Zayed, Omnia and
McCrae, John Philip and
Buitelaar, Paul",
editor = "Beigman Klebanov, Beata and
Shutova, Ekaterina and
Lichtenstein, Patricia and
Muresan, Smaranda and
Wee, Chee",
booktitle = "Proceedings of the Workshop on Figurative Language Processing",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0910",
doi = "10.18653/v1/W18-0910",
pages = "81--90",
abstract = "Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a wide range of applications ranging from text simplification to psychotherapy. Despite the variety of approaches that are trying to process metaphor, there is still a need for better models that mimic the human cognition while exploiting fewer resources. In this paper, we present an approach based on distributional semantics to identify metaphors on the phrase-level. We investigated the use of different word embeddings models to identify verb-noun pairs where the verb is used metaphorically. Several experiments are conducted to show the performance of the proposed approach on benchmark datasets.",
}
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<abstract>Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a wide range of applications ranging from text simplification to psychotherapy. Despite the variety of approaches that are trying to process metaphor, there is still a need for better models that mimic the human cognition while exploiting fewer resources. In this paper, we present an approach based on distributional semantics to identify metaphors on the phrase-level. We investigated the use of different word embeddings models to identify verb-noun pairs where the verb is used metaphorically. Several experiments are conducted to show the performance of the proposed approach on benchmark datasets.</abstract>
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%0 Conference Proceedings
%T Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning
%A Zayed, Omnia
%A McCrae, John Philip
%A Buitelaar, Paul
%Y Beigman Klebanov, Beata
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%S Proceedings of the Workshop on Figurative Language Processing
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F zayed-etal-2018-phrase
%X Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a wide range of applications ranging from text simplification to psychotherapy. Despite the variety of approaches that are trying to process metaphor, there is still a need for better models that mimic the human cognition while exploiting fewer resources. In this paper, we present an approach based on distributional semantics to identify metaphors on the phrase-level. We investigated the use of different word embeddings models to identify verb-noun pairs where the verb is used metaphorically. Several experiments are conducted to show the performance of the proposed approach on benchmark datasets.
%R 10.18653/v1/W18-0910
%U https://aclanthology.org/W18-0910
%U https://doi.org/10.18653/v1/W18-0910
%P 81-90
Markdown (Informal)
[Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning](https://aclanthology.org/W18-0910) (Zayed et al., Fig-Lang 2018)
ACL