Computational Linguistics

Computational Linguistics is an interdisciplinary field that leverages principles from computer science and artificial intelligence to scientifically study and model human language. It focuses on developing formal algorithms and computational systems to understand the structure of language, including its syntax (grammar), semantics (meaning), and phonology (sound). While it provides the theoretical underpinnings for many Natural Language Processing (NLP) applications, computational linguistics is distinctly concerned with using computation as a tool to test linguistic theories and advance the fundamental scientific knowledge of how language is structured and processed.

1. Introduction to Computational Linguistics
   1. Defining the Field
      1. Core Goals and Questions
         1. Modeling Human Language Understanding
         2. Automating Language Processing Tasks
         3. Understanding Language Acquisition and Variation
         4. Bridging Theoretical and Applied Linguistics
      2. The Scientific Study of Language via Computation
         1. Formalization of Linguistic Theories
         2. Simulation and Testing of Hypotheses
         3. Computational Modeling of Language Phenomena
         4. Empirical Validation of Linguistic Claims
   2. Interdisciplinary Foundations
      1. Relationship to Linguistics
         1. Theoretical Linguistics
         2. Descriptive Linguistics
         3. Psycholinguistics
         4. Sociolinguistics
      2. Relationship to Computer Science
         1. Algorithms and Data Structures
         2. Formal Languages and Automata Theory
         3. Software Engineering Principles
         4. Database Systems
      3. Relationship to Artificial Intelligence
         1. Knowledge Representation
         2. Machine Learning
         3. Reasoning and Inference
         4. Symbolic vs. Connectionist Approaches
      4. Relationship to Cognitive Science
         1. Cognitive Modeling
         2. Human Language Processing
         3. Experimental Methods
         4. Computational Psycholinguistics
      5. Relationship to Mathematics and Statistics
         1. Probability Theory
         2. Information Theory
         3. Linear Algebra
         4. Graph Theory
   3. Distinguishing Computational Linguistics from Natural Language Processing
      1. Theoretical Modeling vs. Application Engineering
         1. Focus on Theory Development
         2. Focus on Practical Systems
         3. Research vs. Industry Perspectives
      2. Scientific Inquiry vs. Task-Oriented Performance
         1. Hypothesis Testing
         2. Benchmarking and Task Evaluation
         3. Explanatory vs. Predictive Models
   4. Historical Development
      1. Early Machine Translation Efforts
         1. Georgetown-IBM Experiment
         2. Rule-Based Translation Systems
         3. Limitations and Challenges
      2. The ALPAC Report and its Impact
         1. Funding and Research Shifts
         2. Emphasis on Evaluation
         3. Long-Term Consequences
      3. The Rise of Statistical Methods
         1. Introduction of Probabilistic Models
         2. Data-Driven Approaches
         3. Corpus-Based Research
      4. The Neural Revolution
         1. Emergence of Deep Learning
         2. End-to-End Neural Models
         3. Transformer Architecture Impact