Intelligent Question and Answer and Dialogue System
DOI:
https://doi.org/10.54097/bvzjfa70Keywords:
Intelligent question and answer system; Machine learning; Knowledge graph; Deep learning; Sentiment value analysis.Abstract
This article discusses the development and application of question answering systems in the context of artificial intelligence and natural language processing. It highlights the importance of these systems in resolving user queries and providing accurate and relevant answers. This paper highlights the need to design question answering systems according to established rules and guidelines and use machine learning techniques to improve their performance. It also mentions the emergence of advanced question-answering systems such as IBM Watson, which leverage natural language processing and machine learning to provide more complex answers.
Downloads
References
Arnab Sinha,Zhihong Shen,Yang Song, et al. An Overview of Microsoft Academic Service (MAS) and Applications. Proceedings of the 24th International Conference on World Wide Web. Florence Italy, 2015, p.243-261
Bo Song,Yue Zhuo,Xiaomei Li. Research on Question-Answering System Based on Deep Learning. International Conference on Swarm Intelligence. Shanghai,2018, p.522-529
Dan Tian,Mingchao Li,Qiubing Ren,et al. Intelligent question answering method for construction safety hazard knowledge based on deep semantic mining. Automation in Construction,2023, Volume 145
E.Strickland. IBM Watson, heal thyself: How IBM overpromised and underdelivered on AI health care. IEEE,2019. p. 24-31
Kushal Kafle, Christopher Kanan.Visual question answering: Datasets, algorithms, and future challenges.Computer Vision and Image Understanding,2017,p.3-20
Lu Liu,Jing Luo .A Question Answering System Based on Deep Learning. 14th International Conference. Wuhan, China,2018, p.173-181
Hao Chen, Zepeng Zhai, Fangxiang Feng, et al.Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction.Association for Computational Linguistics,2022,p. 2974-2985
R. Arora, P. Singh, H. Goyal, et al. Comparative Question Answering System based on Natural Language Processing and Machine Learning.2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). Coimbatore, India,2021
Ruijie Wang,Meng Wang,Jun Liu.et al. Leveraging Knowledge Graph Embeddings for Natural Language Question Answering.24th International Conference.Chiang Mai, Thailand,2019,p. 659-675
S. Hu, L. Zou, J. X. Yu.et al. Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs. IEEE,2018, p. 824-837
Weiguo Zheng,Hong Cheng,Jeffrey Xu Yu.et al.Interactive natural language question answering over knowledge graphs.Information Sciences,2019,p. 141-159
X. Cheng, S. Zhu, S. Su and G. Chen.A Multi-Objective Optimization Approach for Question Routing in Community Question Answering Services.IEEE,2017, p. 1779-1792.
Y. Bian and K. Peng. Question Answering System Analysis Based on Machine Learning. IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), Fuzhou, China, 2021
Yashvardhan Sharma,Sahil Gupta .Deep Learning Approaches for Question Answering System.Procedia Computer Science Volume 132,2018,p. 785-794
Yongliang Wu,Shuliang Zhao,Wenbin Li.Phrase2Vec: Phrase embedding based on parsing. Information Sciences Volume 517,2019, p. 100-127
Yongliang Wu,Shuliang Zhao. Community answer generation based on knowledge graph. Information Sciences Volume 545,2021, p. 132-152
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







