Automatic Question Classifiers: A Systematic Review

Silva, Valtemir A.; Bittencourt, Ig Ibert; Maldonado, José Carlos

doi:10.1109/tlt.2018.2878447

Cited by 25 publications

(10 citation statements)

References 66 publications

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“…Previous studies have confirmed that feature selection is helpful for question classification. In 2019, the authors in [1] surveyed studies directly and involved in question classification. They found 88.75% used some extraction/selection mechanism on automatic question classifiers.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Unigram or Bag-of-words (BOW) and Inverse Document Frequency (TF-IDF) are the main techniques for feature extraction and selection [1]. There are advantages to using N-gram in text classification.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Support Vector Machine (SVM) is one of the most popular algorithms with high performance for text classification and question classification. SVM used the vectors approach to classifying related documents [1,26]. Logistic Regression (LR) is a method of statistically analyzing data used to understand the relationship between variables, thereby presenting suitable for general binary classification [20,26].…”

Section: Question Classification Algorithmsmentioning

confidence: 99%

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Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning

Chotirat¹,

Meesad²

2021

Heliyon

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Question classification is a crucial task for answer selection. Question classification could help define the structure of question sentences generated by features extraction from a sentence, such as who, when, where, and how. In this paper, we proposed a methodology to improve question classification from texts by using feature selection and word embedding techniques. We conducted several experiments to evaluate the performance of the proposed methodology using two different datasets (TREC-6 dataset and Thai sentence dataset) with term frequency and combined term frequency-inverse document frequency including Unigram, Unigram+Bigram, and Unigram + Trigram as features. Machine learning models based on traditional and deep learning classifiers were used. The traditional classification models were Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine. The deep learning techniques were Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Hybrid model, which combined CNN and BiLSTM model. The experiment results showed that our methodology based on Part-of-Speech (POS) tagging was the best to improve question classification accuracy. The classifying question categories achieved with average micro 𝐹 1 -score of 0.98 when applied SVM model on adding all POS tags in the TREC-6 dataset. The highest average micro 𝐹 1 -score achieved 0.8 when applied GloVe by using CNN model on adding focusing tags in the Thai sentences dataset.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Section: Question Classification Algorithmsmentioning

confidence: 99%

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Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning

Chotirat¹,

Meesad²

2021

Heliyon

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show abstract

“…The application of Machine Learning to automatic question classifiers was tackled in recent and relevant studies, according to a recent systematic review [3]. Some works indicate the relevance of determining exam questions' importance (weight) [4].…”

Section: Introductionmentioning

confidence: 99%

CLIQ! Intelligent Question Classifier for the elaboration of exams

2022

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“…The task of QC is a restricted sub-task within a broader scope of document classification. The steps taken to build a question classifier is identical to that in a text classification workflow [32], as shown in Fig. 2…”

Section: Feature Extraction For Question Classificationmentioning

confidence: 99%

Question classification via machine learning techniques

Ho¹

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Automatic Question Classifiers: A Systematic Review

Cited by 25 publications

References 66 publications

Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning

Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning

CLIQ! Intelligent Question Classifier for the elaboration of exams

Question classification via machine learning techniques

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