Automatic propbank generation for Turkish

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Tarih

2019-09

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Yayıncı

Incoma Ltd

Erişim Hakkı

info:eu-repo/semantics/openAccess

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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