Page 394 - AI Ver 3.0 Class 11
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2. Differentiate between Rule-based Chatbot and AI-based Chatbot.
Ans.
Factors Rule-based Chatbot AI-based Chatbot
Use Natural Language Processing
Work with established rules and decision (NLP) and Machine Learning methods.
Description trees. Respond to user input using pre- Also known as chat agents or virtual
programmed rules.
assistants.
24-hour access for immediate and
consistent assistance.
Provide personalised interactions
Simple to develop and maintain.
Advantages Respond consistently and accurately to depending on users' preferences and
particular inquiries. history.
Increase productivity and savings by
automating tasks and lowering service
costs.
Struggle with understanding complex Significant development expenditures
language. and resource requirements.
Prone to biases in training data and
Disadvantages a lack of transparency in decision-
Unable to adjust to conditions beyond making.
predetermined rules.
Ethical considerations for privacy,
manipulation, and responsible use.
3. Differentiate between Emotion Detection and Sentiment Analysis.
Ans. Factors Emotion Detection Sentiment Analysis
Definition Identifies various human emotion types. Measures the intensity of an emotion.
Seeks to identify the emotions expressed Sentiment analysis seeks to categorise
Examples
in texts, such as happiness, rage, and grief. data as positive, negative or neutral.
Reading social media content, customer
Applications Assessing user ratings, survey comments.
service chats, etc.
Uses a sliding scale between positive and
AI Training Can be taught to classify emotions. negative e.g. strongly disagree, disagree,
neutral, agree, or strongly agree.
Identifying emotional tokens to Evaluating the overall tone or sentiment
Purpose
understand context. of text date.
4. Explain discourse integration with an example.
Ans. Discourse integration is the process of analysing and identifying the bigger context for a smaller section of natural
language structure (such as a phrase, word, entity or sentence). During this phase, it is critical to ensure that each
phrase, word, entity, or sentence is mentioned in the proper context. This analysis considers sentence structure and
semantics as well as sentence combination and overall text meaning.
For example: Radha wants it.
We can observe from the above sentence that the “it” keyword makes no sense. Hence, this statement is discarded.
In reality, it applies to anything we don't know. Here "it" word depends on the prior sentence, which is not provided.
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