A Neural Question Answering System for Basic Questions about Subroutines

Bansal, Aakash; Eberhart, Zachary; Wu, Lingfei; McMillan, Collin

doi:10.1109/saner50967.2021.00015

Cited by 15 publications

(6 citation statements)

References 63 publications

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“…Input patterns of varying length may be handled by RNNs, resulting in such an excellent choice for applications like statistical analysis of time series, speech recognition, and natural language processing [111]. A quality assurance system answers queries given by human users in natural language [112]. Nevertheless, long-term dependencies in the order of inputs might be difficult for RNNs to record, which can lead to the network forgetting critical information [111].…”

Section: Critical Analysismentioning

confidence: 99%

Deep Learning Based Question Answering System (Survey)

Saeed,

ashraf,

Jhanjhi

2023

Preprint

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Deep learning-based question answering systems have transformed the discipline of natural language processing (NLP) by automating the extraction of answers from textual data. This survey paper provides a captivating overview of these systems, exploring methodologies, techniques, and architectures such as recurrent neural networks (RNNs), BERT model, and transformer models. Extractive and generative approaches are examined, alongside the challenges of handling complex questions, managing noisy input, and addressing rare or unseen words. This survey serves as a stimulating reference, offering valuable insights to researchers and practitioners, fueling innovation and advancement in question answering systems within NLP.

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Section: Critical Analysismentioning

confidence: 99%

Deep Learning Based Question Answering System (Survey)

Saeed,

ashraf,

Jhanjhi

2023

Preprint

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“…QA is an Information Retrieval (IR) system with the user process asking questions in natural language and providing answers in the form of relevant information. QA is a solution for users who want to get information faster [7][8] [9]. Kahaduwa, H. et al developed a question answering system (QA) in the travel domain by analyzing that most traditional question and answer systems require users to search for manual answers or have listed to find relevant answers.…”

Section: Question Answeringmentioning

confidence: 99%

“…So, ontology and predicate-argument structures (PAS) make extracting answers easier. The resulting answer goes through a process where the system ranks the document so that the query will match the keywords obtained [7][8][9] [10].…”

Section: Introductionmentioning

confidence: 99%

Question Answering using Ontology for Sumedang Larang History with Support Vector Machine Based on Telegram Bot

Perangin-Angin

Baizal²,

Richasdy³

2022

mib

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Technological developments affect many aspects, one of which is historical education. History lessons can shape students' personalities and encourage an interest in historical knowledge. There are many stories from Indonesian history, one of which is the Sumedang Larang Kingdom. The Sumedang Larang Kingdom is one of the Islamic kingdoms in Pasundan. However, not many people know about this kingdom. The millennial generation is technologically advanced, so they can take advantage of technological advances to quickly introduce the history of Sumedang Larang. One of them utilizes the telegram bot using the Application Programming Interface (API), which can connect the system to the telegram platform. In addition, this technology can be used as a history learning attraction using the question answering system (QA). Our research aims to build a QA system that can introduce the history of Sumedang Larang to the millennial generation. Because this system uses ontology knowledge with concepts related to the Sumedang Larang domain, it can focus on the history of Sumedang Larang. Applying the support vector machine (SVM) algorithm to process classification text can make it easier to search for text categories. The test results show the performance of the SVM method with a test size parameter of 0.5, such as 74% and 78%. The performance test results are accuracy scores in the subject category and object classification.

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“…A Question Answering System (QAS) is a computerbased system that is designed to understand and respond to questions posed by users in natural language. It aims to provide accurate and relevant answers to user queries by extracting information from a given collection of documents or knowledge sources [6]. The need for intelligent QAS in the medical domain has increased in the last decade, due to the rapid increase of internet users around the world.…”

Section: Introductionmentioning

confidence: 99%

An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM

Mutabazi

Tang

et al. 2023

Applied Sciences

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Question Classification (QC) is the fundamental task for Question Answering Systems (QASs) implementation, and is a vital task, as it helps in identifying the question category. It plays a big role in predicting the answer to a question while building a QAS. However, classifying medical questions is still a challenging task due to the complexity of medical terms. Many researchers have proposed different techniques to solve these problems, but some of these problems remain partially solved or unsolved. With the help of deep learning technology, various text-processing problems have become much easier to solve. In this paper, an improved deep learning-based model for Medical Forum Question Classification (MFQC) is proposed to classify medical questions. In the proposed model, feature representation is performed using Word2Vec, which is a word embedding model. Additionally, the features are extracted from the word embedding layer based on Convolutional Neural Networks (CNNs). Finally, a Bidirectional Long Short Term Memory (BiLSTM) network is used to classify the extracted features. The BiLSTM model analyzes the target information of the representation and then outputs the question category via a SoftMax layer. Our model achieves state-of-the-art performance by effectively capturing semantic and syntactic features from the input questions. We evaluate the proposed CNN-BiLSTM model on two benchmark datasets and compare its performance with existing methods, demonstrating its superiority in accurately categorizing medical forum questions.

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A Neural Question Answering System for Basic Questions about Subroutines

Cited by 15 publications

References 63 publications

Deep Learning Based Question Answering System (Survey)

Deep Learning Based Question Answering System (Survey)

Question Answering using Ontology for Sumedang Larang History with Support Vector Machine Based on Telegram Bot

An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM

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