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

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