Turkish question answering application with course-grained semantic matrix representation of sentences

Dönmez, İlknur; Adalı, Eşref

doi:10.1109/ubmk.2017.8093464

Cited by 3 publications

(1 citation statement)

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“…The previous research on question classification focuses on various tasks of occurrence for relevant class categorization or named entities ( Derici et al, 2015 ). However, they integrated a rule-based method employing an HMM-based sequential categorization method ( Donmez & Adalı, 2017 ). And their answers could not generalise to question classification tasks in an agglutinative language.…”

Section: Discussionmentioning

confidence: 99%

A comparative analysis on question classification task based on deep learning approaches

Zulqarnain

Alsaedi

Ghazali

et al. 2021

PeerJ Computer Science

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Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word embedding with various vector sizes on a large corpus composed of user questions. By comparing analysis, we conducted an experiment on deep learning architectures based on test and 10-cross fold validation accuracy. Experiment results were obtained to illustrate the effectiveness of various Word2vec techniques that have a considerable impact on the accuracy rate using different deep learning approaches. We attained an accuracy of 93.7% by using these techniques on the question dataset.

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Section: Discussionmentioning

confidence: 99%