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It uses Natural Language Processing and Deep Learning concepts, with Naive Bayes as the primary classification technique to identify user intent from text and label-based question answering. The chatbot filters candidates by scoring performance across easy, moderate, and difficult question levels, applying threshold rules to decide advancement or rejection. 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Chatbots are virtual assistant tools designed to simplify the communication between humans and computers. A chatbot will answer your queries and execute a certain computation if required. Chatbots can be developed using Natural Language Processing (NLP) and Deep Learning. Natural Language Process technique like Naïve bayes can be used. Chatbot can be implemented for a fun purpose like chit-chat; these are called Conversational chatbots. Chatbots designed to answer any questions is known as horizontal chatbotsand the specific task-oriented chatbots are known as vertical chatbots (also known as Closed Domain Chatbots). In this paper, we will be discussing a task-oriented chatbot to help recruitment team in the technical round of interview process.  \nKeywords: Closed domain chatbot, Interview Process, Naïve bayes, Natural Language Processing.  \nI. INTRODUCTION  \nThe use of chatbots has increased extensively in recent years. Many industries, hotels, and flight booking companies use virtual agents to communicate with their users. Chatbotsin industries are used for various purposes. Sometimes, they are used to display information. If required, they even help in complex tasks like checking/tracking order status fore-commerce companies. This is the one of the many purposes of task-oriented virtual agents.  \nIn this paper, we will be discussing a task-oriented chatbot, which will be useful for companies to filter out the candidates who are less suited for the job based on their scores which will be determined by the bot. Each question will contribute 10% of the total questions (100%) . 10 questions in each section: Easy, Moderate and Difficult. We are making use of Naive Bayes as a classification technique. Naive Bayes Classifier uses labels to distinguish between different intents. We will store answers for each question in a label that will be used as intent. Naïve Bayes Classifier is easy and fast. It uses probability to classify text. Naïve Bayes is a probabilistic classifier, which means it predicts on the basis of the probability of an object. [1] .  \nManuscript received on 07 June 2022 | Revised Manuscript received on 12 June 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022.  \n* Correspondence Author  \nVineet Agarwal*, System Engineer, Department of Life Science, Tata Consultancy Services, Mumbai (M.H), India.  \nAnjali Shukla, MSCS, Illinois Institute of Technology, Chicago, Illinois, USA.  \n© The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) . This is an open access article under the CC-BY-NC-ND license [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)  \nRetrieval Number: 100.1/ijrte.B70920711222  \nDOI: 10.35940/ijrte.B7092.0711222  \n[Journal Website:](Journal Website: www.ijrte.org)[ ](Journal Website: www.ijrte.org)[www.ijrte.org](Journal Website: www.ijrte.org)  \n46  \nThis algorithm has a frequency table from which likelihood of the word can be predicted; When salutation is being exchanged, if the user says he is excited for the test,“excited”will be classified as a happy emotion with the help of Bayes theorem.  \nThis goal-oriented dialog system will assist the Human-Resources team to eliminate the least suitable candidate as per the company’s requirements. A Pattern matching approach is used in this chatbot to match the answers from the pre-approved answer list which will be stored in the labels. classification method applied in this research is the Naive Bayes method and compared with the Logistic Regression method to determine the class intention [1] .  \nThis paper aims at removing the candidates who score below the threshold. Thus ensuring, candidates with good knowledge of the subject shall be selected for the final interview process hence filter","cbCaiegIYTPUZFXs","https://ap.wps.com/l/cbCaiegIYTPUZFXs","pdf",356709,"English","# Abstract\n# Introduction\n# Working of the Chatbot","[{\"question\":\"What is the main purpose of the task-oriented chatbot in this document?\",\"answer\":\"It helps the recruitment team during the technical round of interviews by filtering candidates based on the bot-determined scores and answers.\"},{\"question\":\"Which techniques are used for intent classification and question answering?\",\"answer\":\"The document describes using Natural Language Processing with Naive Bayes for classification, and pattern matching against a pre-approved answer list stored as labels (intents).\"},{\"question\":\"How does the chatbot conduct the interview and decide candidate outcomes?\",\"answer\":\"It uses three difficulty levels (easy, moderate, difficult). Based on correct-answer percentages and threshold ranges, candidates receive additional questions or end the interview with higher or lower chances of selection.\"}]","Chatbot for Interview - Task-Oriented Chatbot for Recruitment Technical Round | PDF",1790353270]