Hierarchical Directed Acyclic Graph (HDAG) Based Preprocessing Technique for Session Construction

Chitra, S.; Kalpana, B.

doi:10.1007/978-3-642-31552-7_61

Cited by 1 publication

(1 citation statement)

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“…But during the experiment they did not use semantic and syntactic features. In the same way, the integrated approach is known as Hierarchical Directed Acyclic Graph (HDAG) with kernel function implemented by Chitra & Kalpana (2013) the kernel function as known Hierarchical Directed Acyclic Graph (HDAG) which squarely perform some levels of chunks and their relatives on organized natural language database. Also, on the question answering corpus, Xu et al, was introduced a hierarchical technique based on the SNoW learning algorithm for the classification of the questions ( Xu et al, 2019 ).…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%