Testing the role of metadata in metaphor identification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00116301" target="_blank" >RIV/00216224:14330/20:00116301 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.18653/v1/2020.figlang-1.35" target="_blank" >http://dx.doi.org/10.18653/v1/2020.figlang-1.35</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2020.figlang-1.35" target="_blank" >10.18653/v1/2020.figlang-1.35</a>

Result language

angličtina

Original language name

Testing the role of metadata in metaphor identification

Original language description

This paper describes the adaptation and application of a neural network system for the automatic detection of metaphors. The LSTM BiRNN system participated in the shared task of metaphor identification that was part of the Second Workshop of Figurative Language Processing (FigLang2020) held at the Annual Conference of the Association for Computational Linguistics (ACL2020). The particular focus of our approach is on the potential influence that the metadata given in the ETS Corpus of Non-Native Written English might have on the automatic detection of metaphors in this dataset. The article first discusses the annotated ETS learner data, highlighting some of its peculiarities and inherent biases of metaphor use. A series of evaluations follow in order to test whether specific metadata influence the system performance in the task of automatic metaphor identification. The system is available under the APLv2 open-source license.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the Second Workshop on Figurative Language Processing (FigLang2020)

ISBN

9781952148125

ISSN

—

e-ISSN

—

Number of pages

8

Pages from-to

256-263

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg, PA, USA

Event location

Stroudsburg, PA, USA

Event date

Jan 1, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000563422200035