Abstract meaning representation of Turkish

Oral E., Acar A., Eryiğit G.

NATURAL LANGUAGE ENGINEERING, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1017/s1351324922000183
  • Dergi Adı: NATURAL LANGUAGE ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Arts and Humanities Citation Index (AHCI), Social Sciences Citation Index (SSCI), Scopus, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, Linguistics & Language Behavior Abstracts, Psycinfo, DIALNET
  • Anahtar Kelimeler: Abstract meaning representation, Semantic representation, Turkish, SEMANTIC ROLES, ANNOTATION, DEPENDENCY
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

The 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.