@inproceedings{ak-yildiz-2019-automatic,
title = "Automatic {P}ropbank Generation for {T}urkish",
author = "AK, Koray and
Y{\i}ld{\i}z, Olcay Taner",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)",
month = sep,
year = "2019",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/R19-1005",
doi = "10.26615/978-954-452-056-4_005",
pages = "33--41",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automatic Propbank Generation for Turkish
%A AK, Koray
%A Yıldız, Olcay Taner
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F ak-yildiz-2019-automatic
%X 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.
%R 10.26615/978-954-452-056-4_005
%U https://aclanthology.org/R19-1005
%U https://doi.org/10.26615/978-954-452-056-4_005
%P 33-41
Markdown (Informal)
[Automatic Propbank Generation for Turkish](https://aclanthology.org/R19-1005) (AK & Yıldız, RANLP 2019)
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..