Detailed Study of Deep Learning Models for Natural Language Processing

Gupta, Megha; Verma, S; Jain, Priyanshu

doi:10.1109/icacccn51052.2020.9362989

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…Developing effective chatbots requires a solid understanding of deep learning (DL), a subset of ML within AI [17]. DL, which is often associated with deep artificial neural networks, involves sophisticated algorithms that model data through nonlinear function transformations across multiple layers [21]. Deep learning chatbots are primarily categorized as retrieval-based and generative bots.…”

Section: Introductionmentioning

confidence: 99%

Tayseer: A Novel AI-Powered Arabic Chatbot Framework for Technical and Vocational Student Helpdesk Services and Enhancing Student Interactions

Alabbas,

Alomar

2024

Applied Sciences

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The rise of conversational agents (CAs) like chatbots in education has increased the demand for advisory services. However, student–college admission interactions remain manual and burdensome for staff. Leveraging CAs could streamline the admission process, providing efficient advisory support. Moreover, limited research has explored the role of Arabic chatbots in education. This study introduces Tayseer, an Arabic AI-powered web chatbot that enables instant access to college information and communication between students and colleges. This study aims to improve the abilities of chatbots by integrating features into one model, including responding with audiovisuals, various interaction modes (menu, text, or both), and collecting survey responses. Tayseer uses deep learning models within the RASA framework, incorporating a customized Arabic natural language processing pipeline for intent classification, entity extraction, and response retrieval. Tayseer was deployed at the Technical College for Girls in Najran (TCGN). Over 200 students used Tayseer during the first semester, demonstrating its efficiency in streamlining the advisory process. It identified over 50 question types from inputs with a 90% precision in intent and entity predictions. A comprehensive evaluation illuminated Tayseer’s proficiency as well as areas requiring improvement. This study developed an advanced CA to enhance student experiences and satisfaction while establishing best practices for education chatbot interfaces by outlining steps to build an AI-powered chatbot from scratch using techniques adaptable to any language.

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Section: Introductionmentioning

confidence: 99%

Tayseer: A Novel AI-Powered Arabic Chatbot Framework for Technical and Vocational Student Helpdesk Services and Enhancing Student Interactions

Alabbas,

Alomar

2024

Applied Sciences

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“…It enables the machine to read the text, interpret it and determine the important keywords. NLP has the computer ability to process information from a large database of text with very little human intervention [2]. Generally, question classification systems using NLP consist of three main modules: the pre-processing module, the keyword extraction module, and the classification module.…”

Section: Introductionmentioning

confidence: 99%

Semantic Similarity Analysis for Examination Questions Classification Using WordNet

et al. 2023

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Question classification based on Bloom’s Taxonomy (BT) has been widely accepted and used as a guideline in designing examination questions in many institutions of higher learning. The misclassification of questions may happen when the classification task is conducted manually due to a discrepancy in the understanding of BT by academics. Hence, several automated examination question classification systems have been proposed by researchers to perform question classification accurately. Most of this research has focused on specific subject areas only or single-sentence type questions. There has been a lack of research on question classification for multi-sentence type and multi-subject questions. This paper proposes a question classification system (QCS) to perform the examination of question classification using a semantic and synthetic approach. The questions were taken from various subjects of an engineering diploma course, and the questions were either single- or multiple-sentence types. The QCS was developed using a natural language toolkit (NLTK), Stanford POS tagger (SPOS), Stanford parser’s universal dependencies (UD), and WordNet similarity approaches. The QCS used the NLTK to process the questions into sentences and then word tokens, such as SPOS, to tag the word tokens and UD to identify the important word tokens, which were the verbs of the examination questions. The identified verbs were then compared with the BT’s verbs list in terms of word sense using the WordNet similarity approach before finally classifying the questions according to BT. The developed QCS achieved an overall 83% accuracy in the classification of a set of 200 examination questions, according to BT.

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Text-Based Cyberbullying Prevention using Toxicity Filtering Mobile Chat Application and API

Sreedhar

Kumar

Veturi

et al. 2022

2022 IEEE International Conference on Data Science and Information System (ICDSIS)

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Detailed Study of Deep Learning Models for Natural Language Processing

Cited by 6 publications

References 5 publications

Tayseer: A Novel AI-Powered Arabic Chatbot Framework for Technical and Vocational Student Helpdesk Services and Enhancing Student Interactions

Tayseer: A Novel AI-Powered Arabic Chatbot Framework for Technical and Vocational Student Helpdesk Services and Enhancing Student Interactions

Semantic Similarity Analysis for Examination Questions Classification Using WordNet

Text-Based Cyberbullying Prevention using Toxicity Filtering Mobile Chat Application and API

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