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Information Search Mechanisms for Government Entities using Machine Learning and Natural Language Processing Techniques

by Ricardo Ponciano, João Santos, João Isento
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 21
Year of Publication: 2020
Authors: Ricardo Ponciano, João Santos, João Isento
10.5120/ijca2020920150

Ricardo Ponciano, João Santos, João Isento . Information Search Mechanisms for Government Entities using Machine Learning and Natural Language Processing Techniques. International Journal of Computer Applications. 176, 21 ( May 2020), 1-7. DOI=10.5120/ijca2020920150

@article{ 10.5120/ijca2020920150,
author = { Ricardo Ponciano, João Santos, João Isento },
title = { Information Search Mechanisms for Government Entities using Machine Learning and Natural Language Processing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { May 2020 },
volume = { 176 },
number = { 21 },
month = { May },
year = { 2020 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number21/31320-2020920150/ },
doi = { 10.5120/ijca2020920150 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:05.907424+05:30
%A Ricardo Ponciano
%A João Santos
%A João Isento
%T Information Search Mechanisms for Government Entities using Machine Learning and Natural Language Processing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 21
%P 1-7
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, huge quantities of data are produced on the Internet. That data can be used to create added value for the companies. Nevertheless, it is necessary to evaluate the quality of the gathered information in order to avoid the creation of inaccurate insights that can lead to wrong decisions. Thus, on this paper is presented a study of a new search information mechanism for government management entities, which will be able to categorize the retrieved information through the usage of Natural Language Processing and Machine Learning techniques. Web crawling mechanisms are also integrated to gather the information from web sources.

References
  1. B. Marr, “How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read,” Forbes, 2018. [Online]. Available: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#7a5fa33760ba. [Accessed: 25-Feb-2020].
  2. A. Felsberger, B. Oberegger, and G. Reiner, “A Review of Decision Support Systems for Manufacturing Systems,” in The International Conference on Knowledge Technologies and Data-driven Business 2016 - i-KNOW 2016 (i-KNOW 2016), 2016, pp. 1–8.
  3. M. Imran and A. Tanveer, “Decision Support Systems: Creating Value for Marketing Decisions in the Pharmaceutical Insustry,” Eur. J. Bus. Innov. Res., vol. 3, no. 4, pp. 46–65, 2015.
  4. N. Lei and S. K. Moon, “A Decision Support System for Market Segment Driven Product Design,” in Proceedings of the 19th International Conference on Engineering Design (ICED13), Design for Harmonies, Vol.9: Design Methods and Tools, 2013, no. August, pp. 1–10.
  5. A. Bhatia, “A Frame Work for Decision Support System for the Banking Sector – An Empirical Study of State Bank of Patiala,” Int. J. Comp. Tech. Appl., vol. 2, no. 5, pp. 1368–1378, 2011.
  6. A. Lessard, “The Top 5 Advantages of an Effective Performance Management Program,” govloop.com, 2017. [Online]. Available: https://www.govloop.com/community/blog/top-5-advantages-effective-performance-management-program/. [Accessed: 03-Mar-2020].
  7. S. Garr, “Performance Management for Better Business Results,” Bersin, 2011. [Online]. Available: http://www.bersin.com/News/Details.aspx?id=14208. [Accessed: 22-Mar-2020].
  8. N. A. Azemi, H. Zaidi, and N. Hussin, “Information Quality in Organization for Better Decision-Making,” Int. J. Acad. Res. Bus. Soc. Sci., vol. 7, no. 12, 2018.
  9. H. Chen, D. Hailey, N. Wang, and P. Yu, “A Review of Data Quality Assessment Methods for Public Health Information Systems,” Int. J. Environ. Res. Public Heal., vol. 11, pp. 5170–5207, 2014.
  10. L. Daunis, “Poor-Quality Data Imposes Costs and Risks on Businesses, Says New Forbes Insights Report,” Forbes, 2017. [Online]. Available: https://www.forbes.com/sites/forbespr/2017/05/31/poor-quality-data-imposes-costs-and-risks-on-businesses-says-new-forbes-insights-report/. [Accessed: 15-Mar-2020].
  11. D&B, “Transparent Government Demands Robust Data Quality,” D&B, 2009. [Online]. Available: https://www.dnb.com/content/dam/english/dnb-solutions/sales-and-marketing/transparent_government_demands_data_quality.pdf. [Accessed: 20-Mar-2020].
  12. I. MORI, “What do people want, need and expect from public services?,” 2020. [Online]. Available: https://www.ipsos.com/sites/default/files/publication/1970-01/sri_what_do_people_want_need_and_expect_from_public_services_110310.pdf. [Accessed: 21-Mar-2020].
  13. A. Customer Satisfaction Index, “Benchmarks by Sector,” ACSI, 2019. [Online]. Available: https://www.theacsi.org/acsi-benchmarks/benchmarks-by-sector. [Accessed: 17-Oct-2019].
  14. Y. Goldberg, Neural Network Methods for Natural Language Processing. 2017.
  15. D. Jurafsky and J. H. Martin, Speech and Language Procesing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, 2nd ed. 2009.
  16. P. Barba, “Machine Learning for Natural Language Processing,” Lexalytics, 2019. [Online]. Available: https://www.lexalytics.com/lexablog/machine-learning-natural-language-processing. [Accessed: 10-Mar-2019].
  17. M. S. Ahuja, J. S. Bal, and Varnica, “Web Crawler : Extracting the Web Data,” Int. J. Comput. Trends Technol., vol. 13, no. 3, pp. 132–137, 2014.
  18. D. Khurana, A. Koli, K. Khatter, and S. Singh, “Natural Language Processing: State of The Art, Current Trends and Challenges,” no. August 2017, 2018.
  19. Y. Ren, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Almost Unsupervised Text to Speech and Automatic Speech Recognition,” in 36th International Conference on Machine Learning, 2019.
  20. A. Ali and M. Z. Amin, “Conversational AI Chatbot Based on Encoder-Decoder Architectures with Attention Mechanism,” in Artificial Intelligence Festival 2.0, 2019, no. December.
  21. Hubtype, “Rule-Based vs AI Chatbots,” Hubtype, 2019. [Online]. Available: https://www.hubtype.com/blog/rule-based-vs-ai-chatbots/. [Accessed: 25-Mar-2020].
  22. P. Bhavsar, “On Semantic Search,” Medium, 2019. [Online]. Available: https://medium.com/modern-nlp/semantic-search-fuck-yeah-e371c0f639d. [Accessed: 15-Mar-2020].
  23. S. A. Abdul-Kader and J. Woods, “Survey on Chatbot Design Techniques in Speech Conversation Systems,” Int. J. Adv. Comput. Sci. Appl., vol. 6, no. 7, pp. 72–80, 2015.
  24. M. Mnasri, “Recent advances in conversational NLP : Towards the standardization of Chatbot building,” arXiv - Cornell Univ., 2019.
  25. S. Pandiarajan, V. M. Yazhmozhi, and P. P. Kumar, “Semantic Search Engine Using Natural Language Processing,” in Advanced Computer and Communication Engineering Technology, vol. 315, no. November 2015, 2015, pp. 561–571.
  26. K. Kowsari, K. J. Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, “Text classification algorithms: A survey,” Inf., vol. 10, no. 4, pp. 1–68, 2019.
  27. A. Hassan and A. Mahmood, “Efficient deep learning model for text classification based on recurrent and convolutional layers,” Proc. - 16th IEEE Int. Conf. Mach. Learn. Appl. ICMLA 2017, vol. 2017-Decem, no. December 2017, pp. 1108–1113, 2017.
  28. R. S. Devi, D. Manjula, and R. K. Siddharth, “An Efficient Approach for Web Indexing of Big Data through Hyperlinks in Web Crawling,” Sci. World J., vol. 2015, pp. 1–9, 2015.
  29. S. I. Mfenyana, N. Moroosi, M. Thinyane, and S. M. Scott, “Development of a Facebook Crawler for Opinion Trend Monitoring and Analysis Purposes : Case Study of Government Service Delivery in Dwesa,” Int. J. Comput. Appl., vol. 79, no. October, pp. 32–39, 2013.
  30. L. Buitinck et al., “API design for machine learning software: experiences from the scikit-learn project,” ArXiv, pp. 1–15, 2013.
  31. M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16), 2016, pp. 265–283.
  32. A. Gulli and S. Pal, Deep Learning with Keras. Packt Publishing Ltd, 2017.
  33. K. Sahin, “Introduction to Web Scraping With Java,” ScrapingBee, 2019. [Online]. Available: https://www.scrapingbee.com/blog/introduction-to-web-scraping-with-java/. [Accessed: 08-Mar-2020].
  34. R. Gori, “Web Scraping the Java Way,” Stack Abuse, 2019. [Online]. Available: https://stackabuse.com/web-scraping-the-java-way/. [Accessed: 24-Mar-2020].
  35. Jsoup, “jsoup: Java HTML Parser,” Jsoup, 2020. [Online]. Available: https://jsoup.org. [Accessed: 09-Mar-2020].
  36. S. V. Stehman, “Selecting and interpreting measures of thematic classification accuracy,” Remote Sens. Environ., vol. 62, no. 1, pp. 77–89, 1997.
  37. D. M. W. Powers and Ailab, “Evaluation : From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation,” J. Mach. Learn. Technol., vol. 2, no. December, pp. 37–63, 2007.
  38. Y. Zhang, R. Jin, and Z. H. Zhou, “Understanding bag-of-words model: A statistical framework,” Int. J. Mach. Learn. Cybern., vol. 1, no. 1–4, pp. 43–52, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Government Machine Learning Natural Language Processing Classification

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