On the Impact of Temporal Representations on Metaphor Detection

Giorgio Ottolina, Matteo Luigi Palmonari, Manuel Vimercati, Mehwish Alam


Abstract
State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.
Anthology ID:
2022.lrec-1.66
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
623–632
Language:
URL:
https://aclanthology.org/2022.lrec-1.66
DOI:
Bibkey:
Cite (ACL):
Giorgio Ottolina, Matteo Luigi Palmonari, Manuel Vimercati, and Mehwish Alam. 2022. On the Impact of Temporal Representations on Metaphor Detection. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 623–632, Marseille, France. European Language Resources Association.
Cite (Informal):
On the Impact of Temporal Representations on Metaphor Detection (Ottolina et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.66.pdf
Code
 vinid/cade