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Computer Science
Artificial Intelligence
Natural Language Processing (NLP)
Natural Language Processing (NLP)
1. Introduction to Natural Language Processing
2. Linguistic Foundations
3. Text Processing and Preprocessing
4. Language Modeling
5. Feature Representation
6. Word Embeddings and Distributed Representations
7. Classical Machine Learning for NLP
8. Deep Learning Foundations
9. Recurrent Neural Networks
10. Attention Mechanisms and Transformers
11. Pre-trained Language Models
12. Core NLP Applications
13. Advanced Topics
14. Evaluation and Benchmarking
15. Ethics and Responsible AI
Evaluation and Benchmarking
Evaluation Metrics
Classification Metrics
Accuracy and Error Rate
Precision and Recall
F1-Score and F-beta
Area Under Curve
Generation Metrics
BLEU and Variants
ROUGE and Variants
METEOR
BERTScore
Semantic Similarity
Ranking Metrics
Mean Average Precision
Normalized Discounted Cumulative Gain
Mean Reciprocal Rank
Human Evaluation
Annotation Guidelines
Inter-Annotator Agreement
Crowdsourcing
Expert Evaluation
User Studies
Benchmark Datasets
General Language Understanding
GLUE
SuperGLUE
XTREME
Task-Specific Benchmarks
SQuAD for Reading Comprehension
CoNLL for Named Entity Recognition
WMT for Machine Translation
Evaluation Challenges
Dataset Bias
Evaluation Reliability
Metric Limitations
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15. Ethics and Responsible AI