Abstract
Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint the entire domain space. This work presents a bibliometric and literature analysis of QAS. Scopus and Web of Science are two well-known research databases used for the study. A systematic analytical study comprising performance analysis and science mapping is performed. Recent research trends, seminal work, and influential authors are identified in performance analysis using statistical tools on research constituents. On the other hand, science mapping is performed using network analysis on a citation and co-citation network graph. Through this analysis, the domain’s conceptual evolution and intellectual structure are shown. We have divided the literature into four important architecture types and have provided the literature analysis of Knowledge Base (KB)-based and GNN-based approaches for QAS.
Subject
Artificial Intelligence,Computer Science Applications,Information Systems,Management Information Systems
Reference158 articles.
1. Teaching Machines to Read and Comprehend;Hermann;Proceedings of the 28th International Conference on Neural Information Processing Systems,2015
2. Know What You Don’t Know: Unanswerable Questions for SQuAD
3. Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering
4. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding;Devlin;arXiv,2019
5. SQuAD: 100,000+ Questions for Machine Comprehension of Text;Rajpurkar;arXiv,2016
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Brain Tumor Detection Using Advanced Deep Learning Implementations;Traitement du Signal;2023-10-30
2. Cryptocurrency Prediction and Analysis between Supervised and Unsupervised Learning with XAI;2023 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS);2023-10-06
3. Performance evaluation of Transfer Learning models for ASD prediction using non-clinical analysis;Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing;2023-08-03
4. Automatic Facial Expression Recognition using Advanced Transfer Learning;Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing;2023-08-03
5. A System for Language Translation using Sequence-to-sequence Learning based Encoder;2023 International Conference on Emerging Smart Computing and Informatics (ESCI);2023-03-01