Natural Language Processing approach to NLP Meta model automation

Conference paper


Amirhosseini, M.H., Kazemian, H., Ouazzane, K. and Chandler, C. 2018. Natural Language Processing approach to NLP Meta model automation. 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil 08 - 13 Jul 2018 IEEE. doi:10.1109/IJCNN.2018.8489609
AuthorsAmirhosseini, M.H., Kazemian, H., Ouazzane, K. and Chandler, C.
TypeConference paper
Abstract

Neuro Linguistic Programming (NLP) is one of the most utilised approaches for personality development and Meta model is one of the most important techniques in this process. Usually, when one speaks about a problem or a situation, the words that one chooses will delete, distort or generalize portions of their experience. Meta model, which is a set of specific questions or language patterns, can be used to understand and recover the information hidden behind the words used. This technique can be adopted to understand other people’s problems or enable them to understand their own issues better. Applying the Meta Model, however, requires a great level of skill and experience for correct identification of deletion, distortion and generalization. Using the appropriate recovery questions is challenging for NLP practitioners and Psychologists. Moreover, the efficiency and accuracy of existing methods on the Meta model can potentially be hindered by human errors such as personal judgment or lack of experience and skill. This research aims to automate the process of using the Meta Model in conversation in order to eliminate human errors, thereby increasing the efficiency and accuracy of this method. An intelligent software has been developed using Natural Language Processing, with the ability to apply the Meta model techniques during conversation with its user. Comparisons of this software with performance of an established NLP practitioner have shown increased accuracy in identification of the deletion and generalization processes. Recovery of information has also been more efficient in the software in comparison to an NLP practitioner.

KeywordsNeuro Linguistic Programming; Natural Language Processing; Meta Model; Personality Development; NLP
Year2018
Conference2018 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Accepted author manuscript
License
File Access Level
Anyone
Publication dates
Online15 Oct 2018
Publication process dates
Deposited17 Apr 2020
ISSN2161-4407
Book title2018 International Joint Conference on Neural Networks (IJCNN): Proceedings
ISBN978-1-5090-6014-6
Digital Object Identifier (DOI)doi:10.1109/IJCNN.2018.8489609
Web address (URL)https://doi.org/10.1109/IJCNN.2018.8489609
Copyright holder© 2018 IEEE
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Permalink -

https://repository.uel.ac.uk/item/87x53

  • 8
    total views
  • 7
    total downloads
  • 3
    views this month
  • 3
    downloads this month

Export as

Related outputs

Related outputs

Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®
Amirhosseini, M.H. and Kazemian, H. 2020. Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®. Multimodal Technologies and Interaction. 4 (Art. 9).
Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing
Amirhosseini, M.H. and Kazemian, H. 2019. Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing. Cognitive Processing. 20 (2), p. 175–193.