International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12

line decor
  
line decor

 Volume 6, Issue 4 (April 2019), Pages: 1-8

----------------------------------------------

 Original Research Paper

 Title: Intelligent decision support system for CV evaluation based on natural language processing

 Author(s): Hejab Alfawareh 1, *, Shaidah Jusoh 1, 2

 Affiliation(s):

 1Faculty of Computing and IT, Northern Border University, Arar, Saudi Arabia
 2King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-5666-0580

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.04.001

 Abstract:

A curriculum vitae (CV) has become one of the most important documents for applying or hiring any job positions. The CV document is normally assessed by a hiring committee based on some predefined criteria. However, the assessment process is lengthy and fraught with human-engendered bias. To the best of our knowledge, none tool that is able to read and filter CV documents which are presented in texts form had been introduced. The purpose of this research is to create a tool that is able to screen and filter hundreds of CVs automatically. This paper proposed an approach based on natural language techniques to develop the tool. The tool can be considered as a decision support system (DSS) in recruiting new employees. One hundred seventy-eight CV documents were used to test the proposed approach. Obtained results suggest that the proposed approach is successful. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Intelligent decision support system, Natural language processing, CV recommendation system, Human resource management, CV evaluation

 Article History: Received 8 October 2018, Received in revised form 1 February 2019, Accepted 2 February 2019

 Acknowledgement:

This research was funded by Northern Border University, Saudi Arabia, grant number 5757/CIT /2016/F for the project entitled "Intelligent Decision Support System for Hiring Academic Staff based on Natural Language Processing".

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Alfawareh H and Jusoh S (2019). Intelligent decision support system for CV evaluation based on natural language processing. International Journal of Advanced and Applied Sciences, 6(4): 1-8

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 

 Tables

 No Table 

----------------------------------------------

 References (28) 

  1. Abel M, Silva LA, De Ros LF, Mastella LS, Campbell JA, and Novello T (2004). PetroGrapher: Managing petrographic data and knowledge using an intelligent database application. Expert Systems with Applications, 26(1): 9-18. https://doi.org/10.1016/S0957-4174(03)00104-0   [Google Scholar]
  2. Ahmad S and Simonovic SP (2006). An intelligent decision support system for management of floods. Water Resources Management, 20(3): 391-410. https://doi.org/10.1007/s11269-006-0326-3   [Google Scholar]
  3. Aronsky D, Fiszman M, Chapman WW, and Haug PJ (2001). Combining decision support methodologies to diagnose pneumonia. In the AMIA Symposium, American Medical Informatics Association, Bethesda, Maryland, USA: 12-16.   [Google Scholar]
  4. Bohanec M and Rajkovič V (1990). DEX: An expert system shell for decision support. Sistemica, 1(1): 145-157.   [Google Scholar]
  5. Burstein F and Carlsson SA (2008). Decision support through knowledge management. In: Burstein F and Holsapple CW (Eds.), Handbook on decision support systems 1: 103-120. Springer, Berlin, Heidelberg, Germany. https://doi.org/10.1007/978-3-540-48713-5_6   [Google Scholar]
  6. Dasgupta D and Gonzalez FA (2001). An intelligent decision support system for intrusion detection and response. In the International Workshop on Mathematical Methods, Models, and Architectures for Network Security, Springer, Berlin, Heidelberg, Germany: 1-14. https://doi.org/10.1007/3-540-45116-1_1   [Google Scholar]
  7. Demner-Fushman D, Chapman WW, and McDonald CJ (2009). What can natural language processing do for clinical decision support?. Journal of Biomedical Informatics, 42(5): 760-772. https://doi.org/10.1016/j.jbi.2009.08.007   [Google Scholar] PMid:19683066 PMCid:PMC2757540
  8. Doukas H, Patlitzianas KD, Iatropoulos K, and Psarras J (2007). Intelligent building energy management system using rule sets. Building and Environment, 42(10): 3562-3569. https://doi.org/10.1016/j.buildenv.2006.10.024   [Google Scholar]
  9. Froelich J and Ananyan S (2008). Decision support via text mining. In: Burstein F and Holsapple CW (Eds.), Handbook on decision support systems 1: 609-635. Springer, Berlin, Heidelberg, Germany. https://doi.org/10.1007/978-3-540-48713-5_28   [Google Scholar]
  10. Gajzler M (2010). Text and data mining techniques in aspect of knowledge acquisition for decision support system in construction industry. Technological and Economic Development of Economy, 16(2): 219-232. https://doi.org/10.3846/tede.2010.14   [Google Scholar]
  11. Gao S, Wang H, Xu D, and Wang Y (2007). An intelligent agent-assisted decision support system for family financial planning. Decision Support Systems, 44(1): 60-78. https://doi.org/10.1016/j.dss.2007.03.001   [Google Scholar]
  12. Indurkhya N and Damerau FJ (2010). Handbook of natural language processing. Vol. 2, CRC Press, Florida, USA.   [Google Scholar]
  13. Jusoh S and Alfawareh HM (2013). Applying fuzzy sets for opinion mining. In the International Conference on Computer Applications Technology, IEEE, Sousse, Tunisia: 1-5. https://doi.org/10.1109/ICCAT.2013.6521965   [Google Scholar]
  14. Lee CKH, Choy KL, Law KMY, and Ho GTS (2014). Application of intelligent data management in resource allocation for effective operation of manufacturing systems. Journal of Manufacturing Systems, 33(3): 412-422. https://doi.org/10.1016/j.jmsy.2014.02.002   [Google Scholar]
  15. Little JD (2004). Models and managers: The concept of a decision calculus. Management Science, 50(12): 1841-1853. https://doi.org/10.1287/mnsc.1040.0267   [Google Scholar]
  16. Matsatsinis NF and Siskos Y (1999). MARKEX: An intelligent decision support system for product development decisions. European Journal of Operational Research, 113(2): 336-354. https://doi.org/10.1016/S0377-2217(98)00220-3   [Google Scholar]
  17. Pérez IJ, Cabrerizo FJ, and Herrera-Viedma E (2010). A mobile decision support system for dynamic group decision-making problems. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(6): 1244-1256. https://doi.org/10.1109/TSMCA.2010.2046732   [Google Scholar]
  18. Phillips-Wren G, Mora M, Forgionne GA, and Gupta JN (2009). An integrative evaluation framework for intelligent decision support systems. European Journal of Operational Research, 195(3): 642-652. https://doi.org/10.1016/j.ejor.2007.11.001   [Google Scholar]
  19. Prakash N and Sarkar A (2015). Development of an intelligent decision support system for a hierarchical business organization. In the International Conference and Workshop on Computing and Communication, IEEE, Vancouver, Canada: 1-8. https://doi.org/10.1109/IEMCON.2015.7344440   [Google Scholar]
  20. Quintero A, Konaré D, and Pierre S (2005). Prototyping an intelligent decision support system for improving urban infrastructures management. European Journal of Operational Research, 162(3): 654-672. https://doi.org/10.1016/j.ejor.2003.10.019   [Google Scholar]
  21. Salah HA, Mocanu M, and Florea A (2014). Towards an integrated decision support system for the evaluation of water pollution in Tigris Basin (DSSWAPIT). In the 2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, Cluj Napoca, Romania: 391-398. https://doi.org/10.1109/ICCP.2014.6937026   [Google Scholar]
  22. Shen LY, Ochoa JJ, Zhang X, and Yi P (2013). Experience mining for decision making on implementing sustainable urbanization—An innovative approach. Automation in Construction, 29: 40-49. https://doi.org/10.1016/j.autcon.2012.07.001   [Google Scholar]
  23. Sperandio F, Gomes C, Borges J, Brito AC, and Almada-Lobo B (2014). An intelligent decision support system for the operating theater: A case study. IEEE Transactions on Automation Science and Engineering, 11(1): 265-273. https://doi.org/10.1109/TASE.2012.2225047   [Google Scholar]
  24. Turban E and Aronson JE (2001). Expert systems and intelligent systems. Prentice Hall, New Jersey, USA.   [Google Scholar]
  25. Turban E, Sharda R, and Delen D (2010). Decision support and business intelligence systems. Prentice Hall, New Jersey, USA.   [Google Scholar] PMCid:PMC2939501
  26. Wan S and Lei TC (2009). A knowledge-based decision support system to analyze the debris-flow problems at Chen-Yu-Lan River, Taiwan. Knowledge-Based Systems, 22(8): 580-588. https://doi.org/10.1016/j.knosys.2009.07.008   [Google Scholar]
  27. Zadeh LA (1965). Fuzzy sets. Information and Control, 8(3): 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X   [Google Scholar]
  28. Zolnoori M, Zarandi MHF, Moin M, and Teimorian S (2012). Fuzzy rule-based expert system for assessment severity of asthma. Journal of Medical Systems, 36(3): 1707-1717. https://doi.org/10.1007/s10916-010-9631-8   [Google Scholar] PMid:21128097