How does chat bot work?

A chatbot operates using a blend of natural language processing (NLP) and machine learning to understand user inputs and deliver relevant responses.

Steps to Train a Chatbot:

1. 
   

   Import Corpus

   
   
   
   
   
   • Gather the required data that the chatbot will use for training, ensuring it is relevant and comprehensive.
     
   
   
   
   


2. 
   

   Process the Data

   
   
   
   
   
   • Clean the data by removing redundant or irrelevant entries.
     
   
   
   • Ensure the data is well-organized, clear, and beneficial for training.
     
   
   
   
   


3. 
   

   Test Case Handling

   
   
   
   
   
   • Standardize the text by converting it to all uppercase or lowercase.
     
   
   
   • Address potential misinterpretations or misrepresentations to improve accuracy.
     
   
   
   
   


4. 
   

   Tokenization

   
   
   
   
   
   • Break down sentences into individual words.
     
     
     
     
     • Example: "This is a blog" → ["This", "is", "a", "blog"].
       
     
     
     
     
   
   
   
   


5. 
   

   Stemming

   
   
   
   
   
   • Extract the root form of words (etymology).
     
   
   
   • Identify and group similar words across different tenses or variations.
     
     
     
     
     • Example: "running" → "run".
       
     
     
     
     
   
   
   
   


6. 
   

   Generating Bag of Words (BoW)

   
   
   
   
   
   • Represent words as numbers by generating vector embeddings.
     
   
   
   • Perform dot operations to compare vectors for similarity or relationships.
     
   
   
   
   


7. 
   

   One-Hot Encoding

   
   
   
   
   
   • Convert categorical variables into a format that machine learning algorithms can understand.
     
   
   
   • Ensure data is clean and structured for better model performance.
     
   
   
   
   

Architecture :