Uniform Meaning Representation Parsing as a Pipelined Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ASB6IIP9Y" target="_blank" >RIV/00216208:11320/25:SB6IIP9Y - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204887484&partnerID=40&md5=6ba25a0afeb49f72cb3e2f6b5e4182ae" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204887484&partnerID=40&md5=6ba25a0afeb49f72cb3e2f6b5e4182ae</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Uniform Meaning Representation Parsing as a Pipelined Approach

Popis výsledku v původním jazyce

Uniform Meaning Representation (UMR) is the next phase of semantic formalism following Abstract Meaning Representation (AMR), with added focus on inter-sentential relations allowing the representational scope of UMR to cover a full document. This, in turn, greatly increases the complexity of its parsing task with the additional requirement of capturing document-level linguistic phenomena such as coreference, modal and temporal dependencies. In order to establish a strong baseline despite the small size of recently released UMR v1.0 corpus, we introduce a pipeline model that does not require any training. At the core of our method is a two-track strategy of obtaining UMR's sentence and document graphs separately, with the document-level triples being compiled at the token level and the sentence graph being converted from AMR graphs. By leveraging alignment between AMR and its sentence, we are able to generate the first automatic English UMR parses. © 2024 Association for Computational Linguistics.

Název v anglickém jazyce

Uniform Meaning Representation Parsing as a Pipelined Approach

Popis výsledku anglicky

Uniform Meaning Representation (UMR) is the next phase of semantic formalism following Abstract Meaning Representation (AMR), with added focus on inter-sentential relations allowing the representational scope of UMR to cover a full document. This, in turn, greatly increases the complexity of its parsing task with the additional requirement of capturing document-level linguistic phenomena such as coreference, modal and temporal dependencies. In order to establish a strong baseline despite the small size of recently released UMR v1.0 corpus, we introduce a pipeline model that does not require any training. At the core of our method is a two-track strategy of obtaining UMR's sentence and document graphs separately, with the document-level triples being compiled at the token level and the sentence graph being converted from AMR graphs. By leveraging alignment between AMR and its sentence, we are able to generate the first automatic English UMR parses. © 2024 Association for Computational Linguistics.

Druh

D - Stať ve sborníku

CEP obor

—

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 statě ve sborníku

TextGraphs ACL - Proc. TextGraphs-17: Graph-Based Methods Nat. Lang. Process., Annu. Meet. Assoc. Comput. Linguist.

ISBN

979-889176145-2

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

40-52

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

—

Místo konání akce

Bangkok

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—