Empirical Analysis for Unsupervised Universal Dependency Parse Tree Aggregation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AFHKLLZB5" target="_blank" >RIV/00216208:11320/25:FHKLLZB5 - isvavai.cz</a>

Výsledek na webu

<a href="https://arxiv.org/abs/2403.19183" target="_blank" >https://arxiv.org/abs/2403.19183</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.48550/arXiv.2403.19183" target="_blank" >10.48550/arXiv.2403.19183</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Empirical Analysis for Unsupervised Universal Dependency Parse Tree Aggregation

Popis výsledku v původním jazyce

Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks. Parsers' quality often varies depending on the domain and the language involved. Therefore, it is essential to combat the issue of varying quality to achieve stable performance. In various NLP tasks, aggregation methods are used for post-processing aggregation and have been shown to combat the issue of varying quality. However, aggregation methods for post-processing aggregation have not been sufficiently studied in dependency parsing tasks. In an extensive empirical study, we compare different unsupervised post-processing aggregation methods to identify the most suitable dependency tree structure aggregation method.

Název v anglickém jazyce

Empirical Analysis for Unsupervised Universal Dependency Parse Tree Aggregation

Popis výsledku anglicky

Dependency parsing is an essential task in NLP, and the quality of dependency parsers is crucial for many downstream tasks. Parsers' quality often varies depending on the domain and the language involved. Therefore, it is essential to combat the issue of varying quality to achieve stable performance. In various NLP tasks, aggregation methods are used for post-processing aggregation and have been shown to combat the issue of varying quality. However, aggregation methods for post-processing aggregation have not been sufficiently studied in dependency parsing tasks. In an extensive empirical study, we compare different unsupervised post-processing aggregation methods to identify the most suitable dependency tree structure aggregation method.

Druh

J_ost - Ostatní články v recenzovaných periodicích

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

2024

Kód důvěrnosti údajů

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

Název periodika

ArXiv

ISSN

2331-8422

e-ISSN

—

Svazek periodika

2024

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

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EID výsledku v databázi Scopus

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