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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Our plan is to annotate 17,000 sentences by the end of 2017.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper describes our approach to developing the Turkish PropBank by adopting the semantic role-labeling guidelines of the original PropBank and using the translation of the English Penn-TreeBank as a resource.
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Key Findings & Data:In the current phase of the project, more than 9500 sentences are semantically analyzed and predicate-argument information is extracted for 1330 verbs and 1914 verb senses.
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Conclusion & Impact:Our plan is to annotate 17,000 sentences by the end of 2017.
🔬Design:Research Article
📍Scope:2017 Verileri
⚙️Variables:PropBank · semantic role labeling
Abstract
This paper describes our approach to developing the Turkish PropBank by adopting the semantic role-labeling guidelines of the original PropBank and using the translation of the English Penn-TreeBank as a resource. We discuss the semantic annotation process of the PropBank and language-specific cases for Turkish, the tools we have developed for annotation, and quality control for multiuser annotation. In the current phase of the project, more than 9500 sentences are semantically analyzed and predicate-argument information is extracted for 1330 verbs and 1914 verb senses. Our plan is to annotate 17,000 sentences by the end of 2017.
Keywords
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Cite This Article
AK, K., TOPRAK, C., ESGEL, V., YILDIZ, O. T. (2018). Construction of a Turkish proposition bank. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 26 - Sayı: 1, 570–581. https://ajindex.com/articles/187154