![© Dr. Khalid Nazim S.A. B.E., M. Tech, MBA[IT], PhD, LMISTE, LMCSI, MIE
Associate Professor,
Department of Computer Science & Information,
Majmaah University, Az- Zulfi Campus, KSA.
CS 463: Natural Language Processing
© 2025 Dr. Khalid Nazim Abdul Sattar](https://image.slidesharecdn.com/week2introductiontonlp-250218062748-2e8465ac/85/introduction-to-natural-language-processing-1-320.jpg)
introduction to natural language processing
- 1. © Dr. Khalid Nazim S.A. B.E., M. Tech, MBA[IT], PhD, LMISTE, LMCSI, MIE Associate Professor, Department of Computer Science & Information, Majmaah University, Az- Zulfi Campus, KSA. CS 463: Natural Language Processing © 2025 Dr. Khalid Nazim Abdul Sattar
- 2. Week 2 Topics to be Discussed: Define NLP Brief History Purpose & Need of NLP Simple Applications
- 4. Some Buzz-Words NLP – Natural Language Processing CL – Computational Linguistics SP – Speech Processing HLT – Human Language Technology NLE – Natural Language Engineering SNLP – Statistical Natural Language Processing Other Areas: Speech Generation, Text Generation, Speech Understanding, Information Retrieval, Dialogue Processing, Inference, Spelling Correction, Grammar Correction, Text Summarization, Text Categorization
- 5. Brief History of NLP 1940s –1950s: Foundations Development of formal language theory (Chomsky, Backus, Naur, Kleene) Probabilities and information theory (Shannon) 1957 – 1970s: Use of formal grammars as basis for natural language processing (Chomsky, Kaplan) Use of logic and logic-based programming (Minsky, Winograd, Colmerauer, Kay) 1970s – 1983: Probabilistic methods for early speech recognition (Jelinek, Mercer) Discourse modeling (Grosz, Sidner, Hobbs) 1983 – 1993: Finite state models (morphology) (Kaplan, Kay) 1993 – present: Strong integration of different techniques, different areas.
- 6. Natural Language Processing Natural Language Processing—also known as NLP or computational linguistics—is a subfield of Artificial Intelligence (AI), Machine Learning (ML), and linguistics. A branch of AI, it helps computers or machines understand, manipulate, and interpret human language. For several decades now, humans have been communicating with machines through coding and programming languages, which in binary form, constitute of millions of zeroes and ones. According to Gartner, by 2025, nearly 60% of analytical queries will be generated through speech, Natural Language Processing (NLP) or voice, or would be generated automatically.
- 7. Natural Language Processing To define it simply, Natural Language is the natural way in which humans communicate with each other. Today, we have made computers understand this natural language. For example, with voice commands such as “Alexa, what’s the news today” or “Ok Google, play me my favorite track,” communicating with machines has become easier. Similarly, when Siri, Apple’s personal voice assistant, is asked, “What is the cheapest flight to Saudi Arabia tomorrow?”
- 8. Why Should You Care? Two trends An enormous amount of knowledge is now available in machine readable form as natural language text Conversational agents are becoming an important form of human-computer communication Much of human-human communication is now mediated by computers
- 9. Forms of Natural Language The input/output of an NLP system can be: written text speech We will mostly concern with written text (not speech). To process written text, we need: lexical, syntactic, semantic knowledge about the language discourse information, real world knowledge To process spoken language, we need everything required to process written text, plus the challenges of speech recognition and speech synthesis.
- 10. © 2025 Dr. Khalid Nazim Abdul Sattar
- 12. Why NL Understanding is hard? Natural language is extremely rich in form and structure, and very ambiguous. How to represent meaning, Which structures map to which meaning structures. One input can mean many different things. Ambiguity can be at different levels. Lexical (word level) ambiguity -- different meanings of words Syntactic ambiguity -- different ways to parse the sentence Interpreting partial information -- how to interpret pronouns Contextual information -- context of the sentence may affect the meaning of that sentence. Many input’s can mean the same thing. Interaction among components of the input is not clear.
- 13. Knowledge of Language Phonology: concerns how words are related to the sounds that realize them. Morphology: concerns how words are constructed from more basic meaning units called morphemes. A morpheme is the primitive unit of meaning in a language. Syntax: concerns how can be put together to form correct sentences and determines what structural role each word plays in the sentence and what phrases are subparts of other phrases. Semantics: concerns what words mean and how these
- 14. Pragmatics: concerns how sentences are used in different situations and how use affects the interpretation of the sentence. Discourse: concerns how the immediately preceding sentences affect the interpretation of the next sentence. For example, interpreting pronouns and interpreting the temporal aspects of the information. World Knowledge: includes general knowledge about the world. What each language user must know about the other’s beliefs and goals. Knowledge of Language
- 15. NLP - an Inter-Disciplinary Field NLP borrows techniques and insights from several disciplines, namely. Linguistics: How do words form phrases and sentences? What constraints the possible meaning for a sentence? Computational Linguistics: How is the structure of sentences are identified? How can knowledge and reasoning be modeled? Computer Science: Algorithms for automatons, parsers. Engineering: Stochastic techniques for ambiguity resolution. Psychology: What linguistic constructions are easy or difficult for people to learn to use? Philosophy: What is the meaning, and how do words and sentences acquire it?
- 17. Summary The field of Natural Language Processing (NLP) has significantly transformed the way humans interact with machines, enabling more intuitive and efficient communication. NLP encompasses a wide range of techniques and methodologies to understand, interpret, and generate human language. From basic tasks like tokenization and part-of-speech tagging to advanced applications like sentiment analysis and machine translation, the impact of NLP is evident across various domains. As the technology continues to evolve, driven by advancements in machine learning and artificial intelligence, the potential for NLP to enhance human-computer interaction and solve complex language-related challenges remains immense. Understanding the core concepts and applications of Natural Language Processing is crucial for anyone looking to leverage its capabilities in the modern digital landscape.
- 18. © 2025 Dr. Khalid Nazim Abdul Sattar