Leveraging mouse tracking data for part-of-speech and syntactic dependency prediction in Albanian

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AC3A3WAH3" target="_blank" >RIV/00216208:11320/26:C3A3WAH3 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3715669.3726840" target="_blank" >http://dx.doi.org/10.1145/3715669.3726840</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3715669.3726840" target="_blank" >10.1145/3715669.3726840</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Leveraging mouse tracking data for part-of-speech and syntactic dependency prediction in Albanian

Popis výsledku v původním jazyce

High-quality eye-tracking equipment plays an important role in understanding reading behaviors and linguistic processes, such as part-of-speech (PoS) tagging and syntactic dependency parsing. However, this technology is expensive, and data is not always available for under represented languages, such as Albanian. The expense and lack of appropriate equipment makes it difficult to conduct research in reading behavior in Albanian but not only. To address this limitation, we explore the usage of low-cost alternatives to study reading behaviors. Specifically, we leverage mouse tracking data to improve the accuracy of computational linguistic models that predict PoS and syntactic dependencies. The preliminary results from our study suggest that the addition of mouse tracking data, particularly reading time, significantly improves the performance of various linguistic prediction models. These findings emphasizes that mouse tracking data combined with annotated texts provides a viable solution to enhance existing models for low resource languages like Albanian. © 2025 Copyright held by the owner/author(s).

Název v anglickém jazyce

Leveraging mouse tracking data for part-of-speech and syntactic dependency prediction in Albanian

Popis výsledku anglicky

High-quality eye-tracking equipment plays an important role in understanding reading behaviors and linguistic processes, such as part-of-speech (PoS) tagging and syntactic dependency parsing. However, this technology is expensive, and data is not always available for under represented languages, such as Albanian. The expense and lack of appropriate equipment makes it difficult to conduct research in reading behavior in Albanian but not only. To address this limitation, we explore the usage of low-cost alternatives to study reading behaviors. Specifically, we leverage mouse tracking data to improve the accuracy of computational linguistic models that predict PoS and syntactic dependencies. The preliminary results from our study suggest that the addition of mouse tracking data, particularly reading time, significantly improves the performance of various linguistic prediction models. These findings emphasizes that mouse tracking data combined with annotated texts provides a viable solution to enhance existing models for low resource languages like Albanian. © 2025 Copyright held by the owner/author(s).

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

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Eye Track. Res. Appl. Symp. (ETRA)

ISBN

979-8-4007-1487-0

ISSN

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e-ISSN

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Počet stran výsledku

27

Strana od-do

115

Název nakladatele

Association for Computing Machinery

Místo vydání

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Místo konání akce

Tokyo

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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