Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AAJAYHRXJ" target="_blank" >RIV/00216208:11320/23:AJAYHRXJ - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161395398&doi=10.3390%2fmath11112548&partnerID=40&md5=d8d1ceb79982fced175e76b84cd85ef0" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161395398&doi=10.3390%2fmath11112548&partnerID=40&md5=d8d1ceb79982fced175e76b84cd85ef0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/math11112548" target="_blank" >10.3390/math11112548</a>

Result language

angličtina

Original language name

Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation

Original language description

"Correctly identifying multiword expressions (MWEs) is an important task for most natural language processing systems since their misidentification can result in ambiguity and misunderstanding of the underlying text. In this work, we evaluate the performance of the mBERT model for MWE identification in a multilingual context by training it on all 14 languages available in version 1.2 of the PARSEME corpus. We also incorporate lateral inhibition and language adversarial training into our methodology to create language-independent embeddings and improve its capabilities in identifying multiword expressions. The evaluation of our models shows that the approach employed in this work achieves better results compared to the best system of the PARSEME 1.2 competition, MTLB-STRUCT, on 11 out of 14 languages for global MWE identification and on 12 out of 14 languages for unseen MWE identification. Additionally, averaged across all languages, our best approach outperforms the MTLB-STRUCT system by 1.23% on global MWE identification and by 4.73% on unseen global MWE identification. © 2023 by the authors."

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

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

Project

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Continuities

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Publication year

2023

Confidentiality

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

Name of the periodical

"Mathematics"

ISSN

2227-7390

e-ISSN

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Volume of the periodical

11

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

1-18

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85161395398