Abstract meaning representation of Turkish

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AYYB88QUG" target="_blank" >RIV/00216208:11320/25:YYB88QUG - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/22:2PTZLRHA

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129562470&doi=10.1017%2fS1351324922000183&partnerID=40&md5=80f3050cb56fb74d1775c63ed8d82a2e" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129562470&doi=10.1017%2fS1351324922000183&partnerID=40&md5=80f3050cb56fb74d1775c63ed8d82a2e</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/S1351324922000183" target="_blank" >10.1017/S1351324922000183</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Abstract meaning representation of Turkish

Popis výsledku v původním jazyce

meaning representation (AMR) is a graph-based sentence-level meaning representation that has become highly popular in recent years. AMR is a knowledge-based meaning representation heavily relying on frame semantics for linking predicate frames and entity knowledge bases such as DBpedia for linking named entity concepts. Although it is originally designed for English, its adaptation to non-English languages is possible by defining language-specific divergences and representations. This article introduces the first AMR representation framework for Turkish, which poses diverse challenges for AMR due to its typological differences compared to English; agglutinative, free constituent order, morphologically highly rich resulting in fewer word surface forms in sentences. The introduced solutions to these peculiarities are expected to guide the studies for other similar languages and speed up the construction of a cross-lingual universal AMR framework. Besides this main contribution, the article also presents the construction of the first AMR corpus of 700 sentences, the first AMR parser (i.e., a tree-to-graph rule-based AMR parser) used for semi-automatic annotation, and the evaluation of the introduced resources for Turkish. © The Author(s), 2022.

Název v anglickém jazyce

Abstract meaning representation of Turkish

Popis výsledku anglicky

meaning representation (AMR) is a graph-based sentence-level meaning representation that has become highly popular in recent years. AMR is a knowledge-based meaning representation heavily relying on frame semantics for linking predicate frames and entity knowledge bases such as DBpedia for linking named entity concepts. Although it is originally designed for English, its adaptation to non-English languages is possible by defining language-specific divergences and representations. This article introduces the first AMR representation framework for Turkish, which poses diverse challenges for AMR due to its typological differences compared to English; agglutinative, free constituent order, morphologically highly rich resulting in fewer word surface forms in sentences. The introduced solutions to these peculiarities are expected to guide the studies for other similar languages and speed up the construction of a cross-lingual universal AMR framework. Besides this main contribution, the article also presents the construction of the first AMR corpus of 700 sentences, the first AMR parser (i.e., a tree-to-graph rule-based AMR parser) used for semi-automatic annotation, and the evaluation of the introduced resources for Turkish. © The Author(s), 2022.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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 periodika

Natural Language Engineering

ISSN

1351-3249

e-ISSN

—

Svazek periodika

30

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

30

Strana od-do

171-200

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85129562470