Automatic Derivation of Semantic Representations for Thai Serial Verb Constructions: A Grammar-Based Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AE3LPQPPR" target="_blank" >RIV/00216208:11320/25:E3LPQPPR - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204066556&partnerID=40&md5=7e202578440e489de18e4669214d80ae" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204066556&partnerID=40&md5=7e202578440e489de18e4669214d80ae</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Automatic Derivation of Semantic Representations for Thai Serial Verb Constructions: A Grammar-Based Approach

Popis výsledku v původním jazyce

Deep semantic representations are useful for many NLU tasks (Droganova and Zeman, 2019; Schuster and Manning, 2016). Manual annotation to build these representations is time-consuming, and so automatic approaches are preferred (Droganova and Zeman, 2019; Bender et al., 2015). This paper demonstrates how rich semantic representations can be automatically derived for Thai Serial Verb Constructions (SVCs), where the semantic relationship between component verbs is not immediately clear from the surface forms. I present the first fully-implemented, unified analysis for Thai SVCs, deriving appropriate semantic representations (MRS; Copestake et al., 2005) from syntactic features, implemented within a DELPH-IN computational grammar (Slayden, 2009). This analysis increases verified coverage of SVCs by 73% and decreases ambiguity by 46%. The final grammar can be found at: https://github.com/VipashaB94/ThaiGrammar ©2024 Association for Computational Linguistics.

Název v anglickém jazyce

Automatic Derivation of Semantic Representations for Thai Serial Verb Constructions: A Grammar-Based Approach

Popis výsledku anglicky

Deep semantic representations are useful for many NLU tasks (Droganova and Zeman, 2019; Schuster and Manning, 2016). Manual annotation to build these representations is time-consuming, and so automatic approaches are preferred (Droganova and Zeman, 2019; Bender et al., 2015). This paper demonstrates how rich semantic representations can be automatically derived for Thai Serial Verb Constructions (SVCs), where the semantic relationship between component verbs is not immediately clear from the surface forms. I present the first fully-implemented, unified analysis for Thai SVCs, deriving appropriate semantic representations (MRS; Copestake et al., 2005) from syntactic features, implemented within a DELPH-IN computational grammar (Slayden, 2009). This analysis increases verified coverage of SVCs by 73% and decreases ambiguity by 46%. The final grammar can be found at: https://github.com/VipashaB94/ThaiGrammar ©2024 Association for Computational Linguistics.

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

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proc. Annu. Meet. Assoc. Comput Linguist.

ISBN

979-889176097-4

ISSN

0736-587X

e-ISSN

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Počet stran výsledku

16

Strana od-do

422-437

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

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Místo konání akce

Bangkok

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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