Abstract
The goal of my doctoral thesis is to automatically generate interrogative sentences from descriptive sentences of Turkish biology text. We employ syntactic and semantic approaches to parse descriptive sentences. Syntactic and semantic approaches utilize syntactic (constituent or dependency) parsing and semantic role labeling systems respectively. After parsing step, question statements whose answers are embedded in the descriptive sentences are going to be formulated by using some predefined rules and templates. Syntactic parsing is done using an open source dependency parser called MaltParser (Nivre et al. 2007). Whereas to accomplish semantic parsing, we will construct a biological proposition bank (BioPropBank) and a corpus annotated with semantic roles. Then we will employ supervised methods to automatic label the semantic roles of a sentence.
Cite
CITATION STYLE
Soleymanzadeh, K. (2017). Domain specific automatic question generation from text. In ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop (pp. 82–88). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/P17-3014
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