Page 395 - Ai_V3.0_c11_flipbook
P. 395

5.   “A syntax tree is created as part of the procedure to visually represent semantic links.” Identify the phase of NLP
                          processing.                                                                     [CBSE Handbook]
                     Ans.  The phase of NLP processing that involves the creation of a syntax tree to visually represent semantic links is
                          the Syntactical Analysis phase.
                          In syntactical analysis, the aim is to check the grammar, word layouts, and word relationships in a given text. One of
                          the key tasks in this phase is to create a syntax tree, also known as a parse tree, which represents the grammatical
                          structure of the sentence and visually displays the relationships between words. This helps in understanding the
                          syntactical constructs and semantic relationships within the text, thereby aiding in the overall comprehension and
                          analysis of the language.

                 C.   Competency-based/Application-based questions:                     #Problem Solving & Logical Reasoning

                       1.   In “EduTech University”, Professor Geeta envisioned an AI, capable of debating with human-like depth and knowledge.
                          This idea sparked “Project Debator”, motivated by a desire to enhance public discourse through unbiased, well-
                          researched arguments.
                             Professor Geeta wanted the AI to access vast information, articulate points clearly, and anticipate counter arguments.
                          She also envisioned the AI model to learn from interactions, analyse emotional tones, and provide feedback to
                          improve users' debating skills. With this vision, she and her team embarked on developing “Project Debator” to
                          revolutionise how people think, argue, and understand the world. What features would you like to add on from
                          your side, apart from features envisioned by Professor Geeta?

                     Ans.  Professor Geeta's Project Debater aims to create an AI for unbiased debate by providing well-researched arguments,
                          clear communication, and the ability to learn and adapt. To this, you propose adding features like:
                          •   Creative argument approaches using metaphors and storytelling.
                          •   Contextual understanding to tailor arguments for specific audiences.
                          •   Real-time fact-checking for extra credibility.
                          •   Fallacy detection to counter illogical arguments.

                       2.   A ground-breaking AI project was underway at the esteemed “SoftCo Labs”. Dr. Madhavi, the lead researcher, was
                          deep in thought as she looked over her team's latest developments in Natural Language Processing (NLP). They
                          had successfully integrated sentiment analysis into their system, but now faced a crucial decision: should they treat
                          sentiment analysis and emotion detection as separate units?
                           Gathering her team, Dr Madhavi posed a critical question. "Imagine our AI, Ava, navigating the complex world
                          of human interaction. What reasons would justify us dividing sentiment analysis and emotion detection into two
                          distinct units of NLP?"
                           As her team pondered this, they knew they had to consider various factors to ensure Ava could understand and
                          respond to human communication with the utmost accuracy and empathy. What reasons would they come up with
                          to support Dr. Madhavi's proposition?
                     Ans.  Sentiment analysis and emotion detection are two Natural Language Processing (NLP) techniques that use human
                          language to categorise people's thoughts, attitudes, and feelings. Sentiment analysis is a text classification task
                          that determines if subjective information is favourable, negative, or neutral. Emotion detection employs machine
                          learning to examine complicated emotions such as fear, anger, sadness, love, and frustration.
                          At first look, Sentiment Analysis (SA) and Emotion Detection (ED) may appear to be the same concept particularly
                          for individuals without a scientific background. However, they are not synonyms, a simple distinction would be
                          that emotion detection tells the emotion where as sentiment analysis detects the intensity. Emotion detection
                          understands the tokens for emotions where as sentiment analysis looks for the tone to identify the feelings.
                          Assertion and Reasoning Question:
                           Direction: Question 3, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions by
                          selecting the appropriate option given below:
                          a. Both A and R are true and R is the correct explanation of A.
                          b. Both A and R are true but R is not the correct explanation of A.

                                                                   Leveraging Linguistics and Computer Science  393
   390   391   392   393   394   395   396   397   398   399   400