Unit 2568: Computational Linguistics and Natural Language Processing

1. Introduction to Computational Linguistics

• Definition and scope of computational linguistics
• Historical development and milestones
• Key concepts: language models, parsing, semantics
• Applications in Natural Language Processing (NLP) and Artificial Intelligence (AI)

2. Linguistic Fundamentals for Computational Linguistics

• Phonetics and phonology: sounds and sound patterns
• Morphology: word formation and structure
• Syntax: sentence structure and grammar rules
• Semantics: meaning of words and sentences
• Pragmatics: language use in context

3. Corpus Linguistics

• Importance of corpora in computational linguistics
• Corpus design and collection methods
• Annotation: types and tools (POS tagging, syntactic annotation, etc.)
• Corpus analysis techniques and software
• Applications to linguistic research and NLP

4. Natural Language Processing (NLP)

• Text preprocessing: tokenization, normalization
• Part-of-speech (POS) tagging techniques
• Parsing methods: constituency and dependency parsing
• Named Entity Recognition (NER)
• Sentiment analysis basics
• Machine translation overview

5. Machine Learning for NLP

• Overview of machine learning concepts
• Supervised learning: classification, sequence labeling
• Unsupervised learning: clustering, topic modeling
• Deep learning approaches: RNNs, Transformers
• Applications: text classification, language generation

6. Sentiment Analysis

• Sentiment classification methods: lexicon-based vs. machine learning
• Construction and use of sentiment lexicons
• Challenges in sentiment analysis: sarcasm, domain adaptation
• Practical applications: social media monitoring, customer feedback

7. Machine Translation

• Challenges in modeling translation
• Rule-based machine translation (RBMT)
• Statistical machine translation (SMT)
• Neural machine translation (NMT)
• Evaluation metrics: BLEU, METEOR

8. Information Extraction

• Named Entity Recognition (NER) revisited
• Relation extraction techniques
• Event extraction and temporal information
• Tools and frameworks for information extraction

9. Dialogue Systems

• Types: chatbots, virtual assistants, conversational agents
• Dialogue management techniques
• Natural language understanding and generation in dialogue
• Challenges in designing natural language interfaces

10. Ethics and Bias in Computational Linguistics

• Data privacy concerns in NLP
• Recognizing and mitigating bias in datasets and models
• Fairness and transparency in AI systems
• Responsible AI development and deployment