Efficient Dependency Tree Sampling Without Replacement
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ACWH29H64" target="_blank" >RIV/00216208:11320/25:CWH29H64 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197849878&doi=10.18653%2fv1%2f2024.findings-naacl.47&partnerID=40&md5=ecf7a953259f8c99119c7b13054199c7" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197849878&doi=10.18653%2fv1%2f2024.findings-naacl.47&partnerID=40&md5=ecf7a953259f8c99119c7b13054199c7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2024.findings-naacl.47" target="_blank" >10.18653/v1/2024.findings-naacl.47</a>
Alternative languages
Result language
angličtina
Original language name
Efficient Dependency Tree Sampling Without Replacement
Original language description
In the context of computational models of dependency syntax, most dependency treebanks have the restriction that any valid dependency tree must have exactly one edge coming out of the root node in addition to respecting the spanning tree constraints. Many algorithms for dependency tree sampling were recently proposed, both for sampling with and without replacement. In this paper we propose a new algorithm called Wilson Reject SWOR for the case of sampling without replacement by adapting the Wilson Reject algorithm originally created for sampling with replacement and combining it with a Trie data structure. Experimental results indicate the efficiency of our approach in the scenario of sampling without replacement from dependency graphs with random weights. © 2024 Association for Computational Linguistics.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Find. Assoc. Comput. Linguist.: NAACL - Findings
ISBN
979-889176119-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
736-741
Publisher name
Association for Computational Linguistics (ACL)
Place of publication
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Event location
Mexico City
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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