Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing

Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioral patterns and modification of the behavior. A significant part of this process is influenced by the theory of representational systems which equates to the five main senses. The preferred representational system of an individual can explain a large part of exhibited behaviors and characteristics. There are different methods to recognize the representational systems, one of which is to investigate the sensory-based words in the used language during the conversation. However, there are difficulties during this process since there is not a single reference method used for identification of representational systems and existing ones are subject to human interpretations. Some human errors like lack of experience, personal judgment, different levels of skill and personal mistakes may also affect the accuracy and reliability of the existing methods. This research aims to apply a new approach that is to automate the identification process in order to remove human errors, thereby increasing the accuracy and precision. Natural Language Processing has been used for automating this process, and an intelligent software has been developed to identify the preferred representational system with increased accuracy and reliability. This software has been tested and compared to human identification of representational systems. The results of the software are similar to a NLP practitioner, and the software responds more accurately than a human practitioner in various parts of the process. This novel methodology will assist the NLP practitioners to obtain an improved understanding of their clients’ behavioral patterns and the associated cognitive and emotional processes

Neuro linguistic programming automation for improvement of organisational performance

Neuro Linguistic Programming (NLP) is a methodology used for recognition of human behavioural patterns and the modification of the behaviour. A significant part of this process is influenced by the theory of representational systems which based on the five main senses. Meta model is another important technique in this process. This technique can be adopted to allow an individual to gain a better understanding of their own issues as well as those of others. Another vital factor in NLP are Meta programs, which are habitual ways of inputting sorting and filtering the information found in the world around us. The difference in Meta programs results in significant differences in behaviour from one person to another, the type of personality can be recognised through utilising and analysing the Meta programs. There are different methods to predict the personality type based on Meta programs and Myers-Briggs Type Indicator® (MBTI) is currently considered to be one of the most popular and reliable methods. Traditionally, the application of NLP relies on consultation with a profession qualified in implementation of this technique. To circumvent the limitations in reliability of this process, attempts of automation of this technique have been carried out. These attempts aim to eliminate the effect of human error such as lack of skill and experience, inconsistency in judgement, inaccuracy or mistakes as well as the impact of personal opinion. Nonetheless, many shortcomings are integral of the methodologies adopted in these attempts. Primarily, these automations are in the format of computerisation of the NLP practice and no artificial intelligence techniques have been implemented to substitute the role of the human practitioner. Hence, improvement of reliability and accuracy remain a challenge for application of NLP, which this research aims to address using artificial intelligence techniques such as natural language processing. The second challenge in this field is the opportunity of applying NLP to benefit a group of people in order to make NLP applicable for organisations rather than individuals alone. This research aims to create this prospect in order to extend the application of NLP for improvement of organisational performance. The focus of this research is on the automation of the three main branches of NLP, which includes (1) identification of the preferred representational system, (2) the Meta model and (3) personality type prediction based on the Meta programs. Hence, it aims to generate an intelligent software for recognising the preferred representational system and personality type of employees as individuals and also as a group. This recognition offers organisations a specific output of information and relevant advice to improve task allocation, communication and teamwork. Moreover, this research also aims to significantly increase the efficiency, accuracy and reliability of using NLP by substituting the dependence on human judgement by an automated software. Limitations of previous computerisations of NLP are also aimed to be responded to by incorporation of artificial intelligence. To achieve these objectives, the means of analysing the behavioural pattern of individuals by software is to be explored. Moreover, the implementation of natural language processing for identifying the preferred representational system, personality type and application of the NLP Meta model during a human-computer conversation will be investigated. To examine the function of the software and the reliability of its output, three evaluations are to be conducted. Firstly, the results of using the software is to be compared to the use of a questionnaire, which the responses to would be analysed by an experienced NLP practitioner. Both of these methods are to focus on the identification of the preferred representational system. Secondly, the application of the Meta model in a human-computer conversation is to be compared to an NLP practitioner’s analysis of the same conversation. Thirdly, the analysis of personality type is to be evaluated by comparing the use of the intelligent software to the use of a computerised questionnaire. Natural Language Processing and machine learning techniques were used for the automation process and an intelligent software has been developed. The automation is successful in eliminating human errors, thereby the software is able to perform with a higher level accuracy, reliability and efficiency. The performance of the software has been tested and compared to the performance of humans and existing methods. Regarding the representational system identification, the results of the software are similar to an experienced NLP practitioner. However, in various parts of the process, the software responded more accurately than a human practitioner. The results of the automated Meta model have shown increased accuracy in identification of the language patterns used in conversation. The recovery of information has shown to be more efficient in the software in comparison to an NLP practitioner. Finally, the results of the software regarding the personality type prediction was highly accurate and reliable after comparing with an official MBTI questionnaire. The novel methodology created in this research will assist the NLP practitioners and psychologists to obtain an improved understanding of their clients’ behavioural patterns and the associated cognitive and emotional processes. It can also facilitate the organisational performance improvement in organisations

A rule and graph-based approach for targeted identity resolution on policing data

In criminal records, intentional manipulation of data is prevalent to create ambiguous identity and mislead authorities. Registering data electronically can result in misspelled data, variations in naming order, case sensitive data and inconsistencies in abbreviations and terminology. Therefore, trying to obtain the true identity (or identities) of a suspect can be a challenge for law enforcement agencies. We have developed a targeted approach to identity resolution which uses a rule-based scoring system on physical and official identity attributes and a graph-based analysis on social identity attributes to interrogate policing data and resolve whether a specific target is using multiple identities. The approach has been tested on an anonymized policing dataset, used in the SPIRIT project, funded by the European Union’s Horizon 2020. The dataset contains four ‘known’ identities using a total of five false identities. 23 targets were inputted into the methodology with no knowledge of how many or which had false identities. The rule-based scoring system ranked four of the five false identities with the joint highest score for the relevant target name with the remaining false identity holding the joint second highest score for its target. Moreover, when using graph analysis, 51 suspected false identities were found for the 23 targets with four of the five false identities linked through the crimes they had been involved in. Therefore, an identity resolution approach using both a rule-based scoring system and graph analysis, could be effective in facilitating the investigation process for law enforcement agencies and assisting them in finding criminals using false identities

Application of graph-based technique to identity resolution

These days the ability to prove an individual identity is crucial in social, economic and legal aspects of life. Identity resolution is the process of semantic reconciliation that determines whether a single identity is the same when being described differently. The importance of identity resolution has been greatly felt these days in the world of online social networking where personal details can be fabricated or manipulated easily. In this research a new graph-based approach has been used for identity resolution, which tries to resolve an identity based on the similarity of attribute values which are related to different identities in a dataset. Graph analysis techniques such as centrality measurement and community detection have been used in this approach. Moreover, a new identity model has been used for the first time. This method has been tested on SPIRIT policing dataset, which is an anonymized dataset used in SPIRIT project funded by European Union’s Horizon 2020. There are 892 identity records in this dataset and two of them are ‘known’ identities who are using two different names, but they are both belonging to the same person. These two identities were recognized successfully after using the presented method in this paper. This method can assist police forces in their investigation process to find criminals and those who committed a fraud. It can also be useful in other fields such as finance and banking, marketing or customer service