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
- Editors:
- Ruslan Mitkov, Galia Angelova
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/R19-1005.pdf