Natural Language Processing (NLP)

Natural Language Processing (NLP) is a specialized field within artificial intelligence and computer science dedicated to enabling computers to understand, interpret, manipulate, and generate human language. It bridges the gap between human communication and computer data by applying computational models—increasingly based on machine learning and deep learning—to text and speech. Core applications of NLP include machine translation, sentiment analysis, spam detection, and powering conversational agents like chatbots and virtual assistants, making it a cornerstone of modern human-computer interaction.

1. Introduction to Natural Language Processing
   1. Defining NLP
      1. Core Definition and Scope
      2. Computational Linguistics vs NLP
      3. Key Terminology and Concepts
   2. Goals and Applications
      1. Automating Language Understanding
      2. Enabling Human-Computer Communication
      3. Information Extraction and Knowledge Discovery
      4. Language Generation and Summarization
      5. Cross-Lingual Communication
   3. Historical Development
      1. Early Rule-Based Approaches
         1. Handcrafted Grammars
         2. Expert Systems
         3. ELIZA and Early Chatbots
      2. Statistical Revolution
         1. Probabilistic Models
         2. Data-Driven Approaches
         3. Corpus-Based Methods
      3. Neural Network Era
         1. Deep Learning Emergence
         2. Transformer Revolution
         3. Large Language Models
   4. Core Challenges
      1. Ambiguity
         1. Lexical Ambiguity
         2. Syntactic Ambiguity
         3. Semantic Ambiguity
         4. Pragmatic Ambiguity
      2. Language Variability
         1. Multilingual Diversity
         2. Dialectal Variations
         3. Domain-Specific Language
         4. Temporal Language Change
      3. Context and World Knowledge
         1. Commonsense Reasoning
         2. Contextual Understanding
         3. Implicit Information
      4. Data Challenges
         1. Data Sparsity
         2. Low-Resource Languages
         3. Noisy and Unstructured Text
   5. Interdisciplinary Connections
      1. Artificial Intelligence
      2. Machine Learning
      3. Theoretical Linguistics
      4. Cognitive Science
      5. Computer Science
      6. Information Theory