Introduction to Natural Language Processing (NLP)
Download as PPTX, PDF0 likes13 views
Introduction
Introduction to Natural Language Processing (NLP)
- 1. Natural Language Processing NLP Overview
- 2. Outline • Introduction • Stages of Language Processing • Why NLP is hard? • Fields with Connections to NLP • NLP Applications • Factors Changing NLP Landscape • Topics to be covered • Practical Tools
- 3. Introduction • The dialogue above is from ELIZA, an early NLP system (created from 1964 to 1966) that could carry on a limited conversation with a user by imitating the responses of a Rogerian psychotherapist.
- 4. Introduction • ELIZA was one of the first chatterbots and one of the first programs capable of attempting the Turing test (1950).
- 5. Introduction • ChatGPT is included as a co-author in a peer-reviewed paper.
- 6. Introduction • Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language.
- 7. Stages of Language Processing
- 8. Why NLP is hard?
- 9. Why NLP is hard? Ambiguity at multiple levels Phonological: write and right Word senses: bank (finance or river ?) Part of speech: play (noun or verb ?) Syntactic structure: I can see a man with a telescope
- 10. Why NLP is hard?
- 11. Why NLP is hard? Find at least 5 meanings of this sentence: “I made her duck” 1. I cooked waterfowl for her benefit (to eat) 2. I cooked waterfowl belonging to her 3. I created the (plaster?) duck she owns 4. I caused her to quickly lower her head or body 5. I waved my magic wand and turned her into waterfowl
- 12. Why NLP is hard?
- 13. Why NLP is hard?
- 14. Why NLP is hard? • Are there any other reasons?
- 15. Fields with Connections to NLP Linguistics Machine and Deep learning Cognitive science Information theory Logic Data science Political science Psychology Economics Education ......
- 16. NLP Applications
- 17. NLP Applications
- 18. NLP Applications • What is the difference between : Essay Scoring and Short Answer grading Question Answering and Question Generation Extractive and Abstractive summarization
- 19. NLP Applications • Is OCR a natural language processing application?
- 20. Factors Changing NLP Landscape Increases in computing power The rise of the web, then the social web Advances in machine and deep learning Advances in understanding of language in social context
- 21. Topics to be covered
- 22. Topics to be covered NLP Overview Text Pre-Processing Computational Morphology, Syntax, Semantic Language Models NLP with Machine Learning NLP with Deep Learning Word, Sentence, Document Embeddings Seq2Seq and Attention Models Transformers Advanced NLP Applications and Tools
- 23. Topics to be covered Text Pre-Processing: Tokenization Stop Word Removing Normalization Stemming Part of Speech Tagging Parsing
- 24. Topics to be covered Word and Sentence embeddings:
- 25. Topics to be covered Word and Sentence embeddings:
- 26. Topics to be covered Text Similarity:
- 27. Topics to be covered Transformers:
- 28. Practical Tools