Improving Question Classification by Feature Extraction and Selection

Nguyen, Van-Tu; Le, Anh-Cuong

doi:10.17485/ijst/2016/v9i17/93160

Cited by 29 publications

(23 citation statements)

References 10 publications

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“…6 6 Some recent question classifiers have integrated head words, their hypernyms and other semantic features to obtain an accuracy of about 91%. See [9], [10], [11], [12], [13], for more detail.…”

Section: Discussionmentioning

confidence: 99%

A Comparative Study of Neural Network Models for Sentence Classification

Le-Hong

2018

2018 5th NAFOSTED Conference on Information and Computer Science (NICS)

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This paper presents an extensive comparative study of four neural network models, including feed-forward networks, convolutional networks, recurrent networks and long short-term memory networks, on two sentence classification datasets of English and Vietnamese text. We show that on the English dataset, the convolutional network models without any feature engineering outperform some competitive sentence classifiers with rich hand-crafted linguistic features. We demonstrate that the GloVe word embeddings are consistently better than both Skip-gram word embeddings and word count vectors. We also show the superiority of convolutional neural network models on a Vietnamese newspaper sentence dataset over strong baseline models. Our experimental results suggest some good practices for applying neural network models in sentence classification.

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Section: Discussionmentioning

confidence: 99%

A Comparative Study of Neural Network Models for Sentence Classification

Le-Hong

2018

2018 5th NAFOSTED Conference on Information and Computer Science (NICS)

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“…Therefore, in [15], a hierarchical classifier with two levels was proposed for problem classification. A method has been proposed in [18] that are using the feature selection algorithm to evaluate correct features for various types of questions.Though researchers in [19] proposed a SVM-based classifier. In addition; a problem identification method based on SVM was proposed in [20].…”

Section: Question Classification and Its Methodsmentioning

confidence: 99%

Question Classification using a Rule based Model

D*,

et al. 2019

IJITEE

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Question Answering is one of the most common applications for data acquisition. Although the majority of text-mining applications strive to improve the user experience and the tools used to find appropriate answers, the problems still exist because the web content is constantly increasing. The Questions Classification (QC) task is one of the main tasks in improving the classification system is to classify types of questions in the text mining application. A large number of QC methods are introduced to help resolve classification problems, most of which are bag of words approaches. In this project, we propose a QC system that uses Parts of Speech (POS) Tagger and Named Entity Recognition (NER) Tagger from the Stanford core Natural Language Processing (NLP) to classify the questions correctly. We started by cleaning the data by removing the available labels in the questions then we proceed by tagging the questions by splitting words and tagging each and every words in the input question with the POS Tagger. After this step, we will convert them into a pattern without changing the structure of the question. Then we proceed by tagging the question with NER Tagger. Finally, we will do confirmation process for certain question types which is performed by confirming question type module to make the system work efficiently.

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“…In previous work, many researchers focused on particular types of questions. For example, work in [15] focused on the "causal" question type, while work in [2,30,51] focused on factoid questions.…”

Section: Questions Categoriesmentioning

confidence: 99%

“…Support Vector Machine (SVM) is one of the most used algorithms [30], [6], [17], [12], [51], [14], [52]. According to authors in [31] combining an SVM classifier with semantic, syntactic and lexical features improves the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Question categorization and classification using grammar based approach

Mohasseb

Bader-El-Den

Cocea

2018

Information Processing & Management

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Question-answering has become one of the most popular information retrieval applications. Despite that most questionanswering systems try to improve the user experience and the technology used in finding relevant results, many difficulties are still faced because of the continuous increase in the amount of web content. Questions Classification (QC) plays an important role in question-answering systems, with one of the major tasks in the enhancement of the classification process being the identification of questions types. A broad range of QC approaches has been proposed with the aim of helping to find a solution for the classification problems; most of these are approaches based on bag-of-words or dictionaries. In this research, we present an analysis of the different type of questions based on their grammatical structure. We identify different patterns and use machine learning algorithms to classify them. A framework is proposed for question classification using a grammar-based approach (GQCC) which exploits the structure of the questions. Our findings indicate that using syntactic categories related to different domain-specific types of Common Nouns, Numeral Numbers and Proper Nouns enable the machine learning algorithms to better differentiate between different question types. The paper presents a wide range of experiments the results show that the GQCC using J48 classifier has outperformed other classification methods with 90.1% accuracy.

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Improving Question Classification by Feature Extraction and Selection

Cited by 29 publications

References 10 publications

A Comparative Study of Neural Network Models for Sentence Classification

A Comparative Study of Neural Network Models for Sentence Classification

Question Classification using a Rule based Model

Question categorization and classification using grammar based approach

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