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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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