Developing an AI based interactive chatbot or virtual assistant on department of justice website

Developing an AI based interactive chatbot or virtual assistant on department of justice website

R.raja1, G.premchand2, D.chandrpal 3, R.shruthi4,

V.deepika5.

3,Department of Artificial Intelligence and Data Science

1,2,4Department of Computer Science and Engineering

5Department of electronics and communication Engineering

J.N.N Institute of Engineering(Autonomous), Kannigaipair, Tiruvallur, Tamil Nadu, India.

ABSTRACT:

The system will improve user experience on the Department of Justice portal by integrating an AI-based interactive chatbot or virtual agent that can respond well to legal-related questions. The main purpose is to utilize Natural Language Processing (NLP) technologies and machine learning models to provide accurate, context-sensitive, and timely responses by the chatbot. To do this, the system first gathers a detailed dataset from the Justice Department, often in structured formats like.csv or.xlsx files. These datasets have useful information such as previous legal cases, statutes, regulations, and FAQs that the chatbot will pull from to answer users' questions. Prior to using the data for model training, a number of preprocessing steps are required to improve its quality and readiness for machine learning. This involves missing data handling using methods such as imputation or deletion, label encoding to convert categorical variables into numerical form, and text cleaning using a variety of NLP techniques. Text cleaning can include the elimination of extraneous characters, correcting typos, normalization of case forms, and removing stop words that do not help in giving meaning to the query. After cleaning the data and putting it in the required format, it is ready to be used for training the model. For the model itself, classification algorithms like Decision Trees (DT) are utilized, which work well in classifying intricate legal queries into predefined classes. In addition to this, the system incorporates a hybrid model, where it uses a conventional Decision Tree and a Passive Classifier. This hybrid model enables more precise and nuanced predictions by examining numerous decision paths and learning from any existing patterns or relations within the data. This hybrid model seeks to enhance response accuracy by adapting dynamically to varied user inputs so that the AI can reliably respond to an extensive variety of legal queries. Consequently, the chatbot can offer consumers accurate legal advice, help with procedural questions, and sort out the intricacies of legal data, all smoothly and in an easily accessible fashion.

KEYWORDS:

1.         Natural Language Processing (NLP)

2.         Machine Learning (ML)

3.         Conversational AI

4.         Chatbot Development

5.         AI-driven Legal Assistance

6.         Knowledge Base Integration

7.         Government Services Automation

8.         Public Sector AI

9.         Legal Document Assistance

10.       Case Inquiry Support