In this i have developed retrieval based chatbot system using nlp and deeplearning. As I was developed a retrieval-based chatbot, we should define responses as “Intents” in a JSON file to effectively answer questions, search from a knowledge base, and engage in conversations with users, a chatbot needs the ability to understand and interpret user input, determining their intentions and meaning. This understanding is crucial for the chatbot to provide accurate and relevant responses.
The intents I have given:
Packages: nltk==3.7 numpy==1.23.3 tensorflow==2.10.0 joblib==1.2.0 keras==2.10.0 Keras-Preprocessing==1.1.2
This framework provides a structured approach to designing, developing, and deploying chatbot solutions. It outlines the key components and considerations involved in creating an effective and functional chatbot. The database represents the intents of the chatbot.
This are the layers of Chatbot
NLP(Natural languuage processing):
Natural Language Processing or NLP is a prerequisite for our project. NLP allows computers and algorithms to understand human interactions via various languages. In order to process a large amount of natural language data, an AI will definitely need NLP or Natural Language Processing. Currently, we have a number of NLP research ongoing in order to improve the AI chatbots and help them understand the complicated nuances and undertones of human conversations.
Tasks in NLP NLP tasks involve breaking down human text and audio signals from voice data in ways that computers can analyze and convert into comprehensible data. Some of the tasks in NLP data ingestion include:
Speech Recognition: This process involves converting speech into text, a crucial step in speech analysis.
Word Sense Disambiguation: In human speech, a word can have multiple meanings. Word sense disambiguation is a semantic analysis that selects the most
appropriate meaning for a word based on its context
Named Entity Recognition (NER): NER identifies words and phrases as specific entities, such as recognizing “Dev” as a person’s name or “America” as the name of
a country.
Sentiment Analysis: Human speech often contains sentiments and undertones. Extracting these nuances and hidden emotions, like attitude, sarcasm, fear, or joy,
is one of the most challenging tasks undertaken by NLP processes.