Year: 2019
Type: conference-paper
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.
Authors: Koray Ak, Olcay Taner Yıldız
Institution: Işık University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2983993365