Enhancing Turkish Coreference Resolution: Insights from deep learning, dropped pronouns, and multilingual transfer learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AURUAKRCC" target="_blank" >RIV/00216208:11320/26:URUAKRCC - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196493074&doi=10.1016%2fj.csl.2024.101681&partnerID=40&md5=bf81abd209578fe1945668d533bae5bc" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196493074&doi=10.1016%2fj.csl.2024.101681&partnerID=40&md5=bf81abd209578fe1945668d533bae5bc</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.csl.2024.101681" target="_blank" >10.1016/j.csl.2024.101681</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing Turkish Coreference Resolution: Insights from deep learning, dropped pronouns, and multilingual transfer learning
Original language description
Coreference resolution (CR), which is the identification of in-text mentions that refer to the same entity, is a crucial step in natural language understanding. While CR in English has been studied for quite a long time, studies for pro-dropped and morphologically rich languages is an active research area which has yet to reach sufficient maturity. Turkish, a morphologically highly-rich language, poses interesting challenges for natural language processing tasks, including CR, due to its agglutinative nature and consequent pronoun-dropping phenomenon. This article explores the use of different neural CR architectures (i.e., mention-pair, mention-ranking, and end-to-end) on Turkish, a morphologically highly-rich language, by formulating multiple research questions around the impacts of dropped pronouns, data quality, and interlingual transfer. The preparations made to explore these research questions and the findings obtained as a result of our explorations revealed the first Turkish CR dataset that includes dropped pronoun annotations (of size 4K entities/22K mentions), new state-of-the-art results on Turkish CR, the first neural end-to-end Turkish CR results (70.4% F-score), the first multilingual end-to-end CR results including Turkish (yielding 1.0 percentage points improvement on Turkish) and the demonstration of the positive impact of dropped pronouns on CR of pro-dropped and morphologically rich languages, for the first time in the literature. Our research has brought Turkish end-to-end CR performances (72.0% F-score) to similar levels with other languages, surpassing the baseline scores by 32.1 percentage points. © 2024 Elsevier Ltd
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - 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)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer Speech and Language
ISSN
0885-2308
e-ISSN
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Volume of the periodical
89
Issue of the periodical within the volume
2025
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
101681
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85196493074