Abstract meaning representation of Turkish
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A2PTZLRHA" target="_blank" >RIV/00216208:11320/22:2PTZLRHA - isvavai.cz</a>
Výsledek na webu
<a href="https://www.cambridge.org/core/journals/natural-language-engineering/article/abstract-meaning-representation-of-turkish/35E839E5AF1F7B9F6BF16275A44BB71D" target="_blank" >https://www.cambridge.org/core/journals/natural-language-engineering/article/abstract-meaning-representation-of-turkish/35E839E5AF1F7B9F6BF16275A44BB71D</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1017/S1351324922000183" target="_blank" >10.1017/S1351324922000183</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Abstract meaning representation of Turkish
Popis výsledku v původním jazyce
Abstract 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.
Název v anglickém jazyce
Abstract meaning representation of Turkish
Popis výsledku anglicky
Abstract 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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
—
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Natural Language Engineering
ISSN
1351-3249
e-ISSN
1469-8110
Svazek periodika
—
Číslo periodika v rámci svazku
2022-4-28
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
30
Strana od-do
1-30
Kód UT WoS článku
000792144500001
EID výsledku v databázi Scopus
2-s2.0-85129562470