A hybrid approach for question classification in Persian automatic question answering systems

Sherkat, Ehsan; Farhoodi, Mojgan

doi:10.1109/iccke.2014.6993377

Cited by 12 publications

(5 citation statements)

References 5 publications

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“…From Table 3 and Table (4) we find that there is an improvement in performance in most of the comparison criteria, which confirms the importance and effectiveness of using the genetic algorithm and its effective role in improving the classification in different criteria. Algorithm comparison results on sub-labeled data using genetics.…”

Section: Resultssupporting

confidence: 60%

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Improve Question Classification Genetic Algorithm Based Feature Selection and Convolution Neural Network

Shama

2024

basjs

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Natural Language Processing (NLP) approaches play a crucial role in classifying inquiries and comprehending human language in diverse applications. A Question Answering System (QAS) consists of three components are question processing, information retrieval, and answer selection. Question Answering Systems (QASs) are a distinct form of information retrieval. The most crucial aspect of QAS is deciding on the question type since it influences the other sections following. However, an important question-answering system requires a prominent question classification system. In the past, there are different methods to solve this problem, such as rule-based learning, and hybrid approaches. However, the problem with these methods is that the rules require a lot of effort to create and are very limited. In this study, the utilization of genetic algorithm and deep neural network techniques enhances the quality control problem-solving process. This research utilizes the UIUC dataset. This collection comprises 5452 questions designed for learning purposes and an additional 500 questions specifically intended for assessment. The suggested solution involves converting each query into a matrix, with each row representing the Word2vec of a word. Subsequently, a Genetic Algorithm (GA) is employed to identify the most optimal features. Ultimately, a Convolutional Neural Network is utilized for classification, yielding a remarkable accuracy of 98.2% in our experimentation with the question dataset.

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Section: Resultssupporting

confidence: 60%

“…There are two types of traditional question classification methods: rulebased methods and statistical machine learning methods. Early rule-based methods mainly used artificial analysis of syntactic structure to derive rules and then judge the question type [4]. Our method has many features .…”

Section: Introductionmentioning

confidence: 99%

Improve Question Classification Genetic Algorithm Based Feature Selection and Convolution Neural Network

Shama

2024

basjs

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“…Moreover, to resolve the question classification issues, there are several different machine learning approaches such as Neural Network, Random Forest, SVM, Decision Trees, Naive Base, KNN that have been applied for classifications. However, for the question classification task in NLP, the SVM (“Support Vector Machine”) considers the key approach in machine learning ( Sherkat & Farhoodi, 2014 ). To obtain their objectives, the authors Sherkat & Farhoodi (2014) used the SVM and dimension reduction method which uses a few linguistic features with a bag of the n-grams feature vector.…”

Section: Related Workmentioning

confidence: 99%

“…However, for the question classification task in NLP, the SVM (“Support Vector Machine”) considers the key approach in machine learning ( Sherkat & Farhoodi, 2014 ). To obtain their objectives, the authors Sherkat & Farhoodi (2014) used the SVM and dimension reduction method which uses a few linguistic features with a bag of the n-grams feature vector. Similarly, ( Huang et al, 2017 ) applied a tree kernel with an SVM for identification the questions answering and successfully achieved an accuracy of 87.4% statistics.…”

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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“…• Ehsan sherkat [18] programmed address replying is the assignment of finding the precise reply of a address composed in common dialect. Classification plays a major part in programmed address reply framework.…”

Section: Literature Survey A)mentioning

confidence: 99%

Automatic Question Paper Generation using ML: A Review

Dhomse¹

2021

TURCOMAT

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In any instructive field educational programs, the courses are characterized with learning goals. It is exceptionally challenging for the instructors to create Question Papers with changed questions. There's no standardizing strategy to guarantee quality of question paper. Consequently, there arises a have to be have a framework which can consequently produce it from we entered information inside few seconds. It’s basic to specify tests and exams, it plans understudies in their journey for information, so having a legitimate examination paper & organize is essential. Now the traditional strategy of producing address paper has been manual and it is exceptionally time and cost expending to diminish these things we developed this framework which is able make the programmed address paper and this it can be valuable to numerous instructive establishing.

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A hybrid approach for question classification in Persian automatic question answering systems

Cited by 12 publications

References 5 publications

Improve Question Classification Genetic Algorithm Based Feature Selection and Convolution Neural Network

Improve Question Classification Genetic Algorithm Based Feature Selection and Convolution Neural Network

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

Automatic Question Paper Generation using ML: A Review

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