Automatic Propbank Generation for Turkish

Koray AK, Olcay Taner Yıldız


Abstract
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.
Anthology ID:
R19-1005
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
33–41
Language:
URL:
https://aclanthology.org/R19-1005
DOI:
10.26615/978-954-452-056-4_005
Bibkey:
Cite (ACL):
Koray AK and Olcay Taner Yıldız. 2019. Automatic Propbank Generation for Turkish. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 33–41, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Automatic Propbank Generation for Turkish (AK & Yıldız, RANLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/R19-1005.pdf