Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3APPL8V2SZ" target="_blank" >RIV/00216208:11320/23:PPL8V2SZ - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172014448&doi=10.1007%2f978-3-031-40498-6_6&partnerID=40&md5=4028296fb87c614e412d23ab8d6349f6" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172014448&doi=10.1007%2f978-3-031-40498-6_6&partnerID=40&md5=4028296fb87c614e412d23ab8d6349f6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-40498-6_6" target="_blank" >10.1007/978-3-031-40498-6_6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines

Popis výsledku v původním jazyce

"This paper presents a set of industrial-grade text processing models for Hungarian that achieve near state-of-the-art performance while balancing resource efficiency and accuracy. Models have been implemented in the spaCy framework, extending the HuSpaCy toolkit with several improvements to its architecture. Compared to existing NLP tools for Hungarian, all of our pipelines feature all basic text processing steps including tokenization, sentence-boundary detection, part-of-speech tagging, morphological feature tagging, lemmatization, dependency parsing and named entity recognition with high accuracy and throughput. We thoroughly evaluated the proposed enhancements, compared the pipelines with state-of-the-art tools and demonstrated the competitive performance of the new models in all text preprocessing steps. All experiments are reproducible and the pipelines are freely available under a permissive license. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG."

Název v anglickém jazyce

Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines

Popis výsledku anglicky

"This paper presents a set of industrial-grade text processing models for Hungarian that achieve near state-of-the-art performance while balancing resource efficiency and accuracy. Models have been implemented in the spaCy framework, extending the HuSpaCy toolkit with several improvements to its architecture. Compared to existing NLP tools for Hungarian, all of our pipelines feature all basic text processing steps including tokenization, sentence-boundary detection, part-of-speech tagging, morphological feature tagging, lemmatization, dependency parsing and named entity recognition with high accuracy and throughput. We thoroughly evaluated the proposed enhancements, compared the pipelines with state-of-the-art tools and demonstrated the competitive performance of the new models in all text preprocessing steps. All experiments are reproducible and the pipelines are freely available under a permissive license. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG."

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

"Lect. Notes Comput. Sci."

ISBN

978-303140497-9

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

58-69

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

—

Místo konání akce

Cham

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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