P-1.1.9.ppt
- 1. DISCOVER . LEARN . EMPOWER UNIVERSITY INSTITUTE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGG. Bachelor of Engineering (Computer Science & Engineering) Artificial Intelligence and Machine Learning(21CSH-316) Prepared by: Sitaram patel(E13285) 1 www.cuchd.in Computer Science and Engineering Department
- 2. Course Outcomes 2 CO1 -Understand the fundamental concepts and techniques of artificial intelligence and machine learning.
- 3. Course Objectives 3 To understand the history and development of Machine Learning. To provide a comprehensive foundation to Machine Learning and Optimization methodology with applications t. To study learning processes: supervised and unsupervised, deterministic and statistical knowledge of Machine learners, and ensemble learning To understand modern techniques and practical trends of Machine learning.
- 4. Applications of NLP • There are the following applications of NLP - • 1. Question Answering • Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language. • Text and speech processing: This includes Speech recognition, text-&-speech processing, encoding(i.e converting speech or text to machine-readable language), etc. • Text classification: This includes Sentiment Analysis in which the machine can analyze the qualities, emotions, and sarcasm from text and also classify it accordingly. • Language generation: This includes tasks such as machine translation, summary writing, essay writing, etc. which aim to produce coherent and fluent text. • Language interaction: This includes tasks such as dialogue systems, voice assistants, and chatbots, which aim to enable natural communication between humans and computers. 4
- 5. Phases of NLP There are the following five phases of NLP: 5
- 6. Applications of NLP • 1. Lexical Analysis and Morphological • The first phase of NLP is the Lexical Analysis. This phase scans the source code as a stream of characters and converts it into meaningful lexemes. It divides the whole text into paragraphs, sentences, and words. • 2. Syntactic Analysis (Parsing) • Syntactic Analysis is used to check grammar, word arrangements, and shows the relationship among the words. • Example: Agra goes to the Poonam • In the real world, Agra goes to the Poonam, does not make any sense, so this sentence is rejected by the Syntactic analyzer. • 3. Semantic Analysis • Semantic analysis is concerned with the meaning representation. It mainly focuses on the literal meaning of words, phrases, and sentences. 6
- 7. 4. Discourse Integration Discourse Integration depends upon the sentences that proceeds it and also invokes the meaning of the sentences that follow it. 5. Pragmatic Analysis Pragmatic is the fifth and last phase of NLP. It helps you to discover the intended effect by applying a set of rules that characterize cooperative dialogues. For Example: "Open the door" is interpreted as a request instead of an order. 7
- 8. References • Text Books: • 1. Rich E., Artificial Intelligence, Tata McGrawHills. • 2. George F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Pearson Education Asia. • Reference Material: • 1. D.W. Patterson, Introduction to AI and Expert Systems, PHI. • 2. N.J. Nilsson, Principles of Artificial Intelligence, Kaufmann,1980 • 3. Saroj Kaushik, Logic and Prolog Programming, New Age International Publications. • 4. PH.Winston, Artificial Intelligence, Addison Wesley. • WEB LINKS https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_natural_language_process ing.htm https://www.geeksforgeeks.org/natural-language-processing-overview/ VIDEO LINKS https://www.youtube.com/watch?v=5hKxvh4RAsY&list=PLyqSpQzTE6M_EcNgdZ2qOtTZe7YI4Eedb 8
- 9. THANK YOU