Beyond Single Parsers: An Empirical Analysis of Dependency Parse Tree Aggregation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AV9X2HZNU" target="_blank" >RIV/00216208:11320/26:V9X2HZNU - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-981-96-8197-6_27" target="_blank" >http://dx.doi.org/10.1007/978-981-96-8197-6_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-96-8197-6_27" target="_blank" >10.1007/978-981-96-8197-6_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Beyond Single Parsers: An Empirical Analysis of Dependency Parse Tree Aggregation

Popis výsledku v původním jazyce

Dependency parsing is essential in Natural Language Processing (NLP), but parser performance varies across languages and domains, especially in low-resource settings. While aggregation methods have improved other NLP tasks, their role in dependency parsing remains largely unexplored. This study evaluates three unsupervised aggregation frameworks: Maximum Spanning Tree (MST), Conflict Resolution on Heterogeneous Data (CRH), and a Customized Ising Model (CIM), using 71 Universal Dependency test treebanks covering 49 languages. Results show that the CIM consistently outperforms individual parsers and other aggregation approaches by effectively estimating parser quality. These findings highlight the potential of parse tree aggregation for improving parsing robustness in multilingual and low-resource settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Název v anglickém jazyce

Beyond Single Parsers: An Empirical Analysis of Dependency Parse Tree Aggregation

Popis výsledku anglicky

Dependency parsing is essential in Natural Language Processing (NLP), but parser performance varies across languages and domains, especially in low-resource settings. While aggregation methods have improved other NLP tasks, their role in dependency parsing remains largely unexplored. This study evaluates three unsupervised aggregation frameworks: Maximum Spanning Tree (MST), Conflict Resolution on Heterogeneous Data (CRH), and a Customized Ising Model (CIM), using 71 Universal Dependency test treebanks covering 49 languages. Results show that the CIM consistently outperforms individual parsers and other aggregation approaches by effectively estimating parser quality. These findings highlight the potential of parse tree aggregation for improving parsing robustness in multilingual and low-resource settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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

Lect. Notes Comput. Sci.

ISBN

978-981-96-8196-9

ISSN

—

e-ISSN

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Počet stran výsledku

13

Strana od-do

362-374

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

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Místo konání akce

Sydney

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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