Automatic propbank generation for Turkish
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Dosyalar
Tarih
2019-09
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Incoma Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Semantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears a semantic role. It provides an extensive dataset to enhance NLP applications such as information retrieval, machine translation, information extraction, and question answering. However, creating SRL models are difficult. Even in some languages, it is infeasible to create SRL models that have predicate-argument structure due to lack of linguistic resources. In this paper, we present our method to create an automatic Turkish PropBank by exploiting parallel data from the translated sentences of English PropBank. Experiments show that our method gives promising results. © 2019 Association for Computational Linguistics (ACL).
Açıklama
Anahtar Kelimeler
Argument structures, Computational linguistics, Deep learning, Dependency parser, Linguistic resources, Machine translations, Natural language processing systems, Natural languages, Parallel data, Question answering, Semantic role labeling, Semantic roles, Semantics, Syntactics, Translation (languages)
Kaynak
International Conference Recent Advances in Natural Language Processing, RANLP
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
2019
Sayı
Künye
Ak, K. & Yıldız, O. T. (2019). Automatic propbank generation for Turkish. Paper presented at the International Conference Recent Advances in Natural Language Processing, RANLP, 33-41. doi:10.26615/978-954-452-056-4_005
