A corpus-based approach to generalizing a chatbot system in python

 

A corpus-based approach to generalizing a chatbot system.

 

 PROJECT ID: PYTHON01

 

PROJECT NAME: A corpus-based approach to generalizing a chatbot system.

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

A chatbot is a conversational agent capable of answering user queries in the form of text, speech, or via a graphical user interface. In simple words, a chatbot is a software application that can chat with a user on any topic. Chatbots can be broadly categorized into two types: Task-Oriented Chatbots and General Purpose Chatbots.

The task-oriented chatbots are designed to perform specific tasks. For instance, a task-oriented chatbot can answer queries related to train reservation, pizza delivery; it can also work as a personal medical therapist or personal assistant.

On the other hand, general purpose chatbots can have open-ended discussions with the users.

There is also a third type of chatbots called hybrid chatbots that can engage in both task-oriented and open-ended discussion with the users.

PROJECT MODULES:

Learning-Based Chatbots

Learning-based chatbots are the type of chatbots that use machine learning techniques and a dataset to learn to generate a response to user queries. Learning-based chatbots can be further divided into two categories: retrieval-based chatbots and generative chatbots.

The retrieval based chatbots learn to select a certain response to user queries. On the other hand, generative chatbots learn to generate a response on the fly.

One of the main advantages of learning-based chatbots is their flexibility to answer a variety of user queries. Though the response might not always be correct, learning-based chatbots are capable to answer to any type of user query. One of the major drawbacks of these chatbots is that they may need a huge amount of time and data to train.

Rule-Based Chatbots

Rule-based chatbots are pretty straight forward as compared to learning-based chatbots. There are a specific set of rules. If the user query matches any rule, the answer to the query is generated, otherwise the user is notified that the answer to user query doesn't exist.

One of the advantages of rule-based chatbots is that they always give accurate results. However, on the downside, they do not scale well. To add more responses, you have to define new rules.

In the following section, I will explain how to create a rule-based chatbot that will reply to simple user queries regarding the sport of tennis.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Conclusion

Chatbots are conversational agents that engage in different types of conversations with humans. Chatbots are finding their place in different strata of life ranging from personal assistant to ticket reservation systems and physiological therapists. Having a chatbot in place of humans can actually be very cost effective. However, developing a chatbot with the same efficiency as humans can be very complicated.

In this article, we show how to develop a simple rule-based chatbot using cosine similarity. In the next article, we explore some other natural language processing arena.

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

 

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