Deep Learning-Based Preprocessing Tools for Turkish Natural Language Processing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AIXIBKYLA" target="_blank" >RIV/00216208:11320/25:IXIBKYLA - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202632922&doi=10.1007%2f978-3-031-66705-3_15&partnerID=40&md5=e4b7a13018e011e2fe856762b6688db3" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202632922&doi=10.1007%2f978-3-031-66705-3_15&partnerID=40&md5=e4b7a13018e011e2fe856762b6688db3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-66705-3_15" target="_blank" >10.1007/978-3-031-66705-3_15</a>

Result language

angličtina

Original language name

Deep Learning-Based Preprocessing Tools for Turkish Natural Language Processing

Original language description

As the demand for effective natural language processing applications in Turkish continues to rise, the need for text preprocessing tools tailored to the Turkish language increases. These tools form the initial step of any natural language application and improves the efficiency of complex tasks such as text summarization, question-answering, and machine translation. We propose a novel deep learning-based framework focusing on Turkish preprocessing tasks, including tokenization, sentence splitting, deasciification, part-of-speech tagging, vowelization, spell correction, and morphological analysis. The proposed framework is suitable for independent use of each preprocessing tool as well as the use in an all-in-one scheme. We use the CANINE model to train the character-level tools, and BERT and mT5 models for the token-based tools. We evaluate the framework for each task on the BOUN Treebank in the UD project and make both the tools and the codes publicly available. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Czech name

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Czech description

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Type

D - Article in proceedings

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

2024

Confidentiality

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

Article name in the collection

Commun. Comput. Info. Sci.

ISBN

978-303166704-6

ISSN

1865-0929

e-ISSN

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Number of pages

17

Pages from-to

218-234

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

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Event location

Dijon

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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