Challenges in Natural Language Processing (NLP): Limits, Risks & Opportunities
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Course Modules:
Module 1: Linguistic Ambiguity and Complexity
Lexical, syntactic, and semantic ambiguity
Word sense disambiguation (WSD)
Contextual understanding and misinterpretation
Module 2: Multilingual and Cross-Language NLP
Machine translation challenges
Low-resource languages and zero-shot learning
Code-switching and dialect variation
Module 3: Bias and Fairness in NLP
Bias in training corpora (gender, race, nationality)
Harmful associations and offensive outputs
Fairness-aware language modeling and detection
Module 4: Data Quality and Annotation Issues
Noisy and inconsistent data
Annotator disagreement and label bias
Domain adaptation and generalization problems
Module 5: Real-Time and Scalable NLP
Latency in NLP applications (e.g., chatbots, transcription)
Memory and compute constraints
Trade-offs between model size and speed
Module 6: Explainability and Trust in NLP Models
Why LLMs like GPT, BERT can be opaque
Using SHAP, LIME, and attention visualization
Communicating NLP model decisions clearly
Module 7: Capstone Project – Solve a Real-World NLP Challenge
Choose one challenge (e.g., disambiguation, bias, multilingual support)
Propose a solution using current tools or models
Submit a working notebook and strategy presentation
Tools & Technologies Used:
Python, Hugging Face Transformers
NLTK, spaCy, Gensim
SHAP, LIME, multilingual datasets (e.g., XNLI, Tatoeba)
Target Audience:
NLP practitioners and AI engineers
Linguists and language researchers
Developers building chatbots, translators, or voice tools
Students and professionals entering the field of NLP
Global Learning Benefits:
Design more robust and inclusive NLP systems
Anticipate failure points in real-world applications
Build cross-linguistic, ethical, and scalable NLP solutions
Master tools to audit and optimize language models
🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI
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