Unsupervised NLP
Unsupervised learning methods and techniques in Natural Language Processing
Unsupervised NLP
Unsupervised learning in NLP focuses on discovering patterns and structures in text data without labeled examples.
Topic Modeling
Latent Dirichlet Allocation
- Probabilistic model
- Document-topic distribution
- Topic-word distribution
- Hyperparameter tuning
Non-negative Matrix Factorization
- Matrix decomposition
- Topic coherence
- Interpretability
- Implementation strategies
Text Clustering
Document Clustering
- K-means clustering
- Hierarchical clustering
- DBSCAN
- Evaluation metrics
Semantic Clustering
- Embedding-based clustering
- Semantic similarity
- Cluster interpretation
- Applications
Word Embeddings
Unsupervised Word Representations
- Word2Vec
- FastText
- GloVe
- Training strategies
Contextual Embeddings
- BERT-based embeddings
- Auto-encoding models
- Contrastive learning
- Fine-tuning approaches
Document Similarity
Similarity Metrics
- Cosine similarity
- Euclidean distance
- Jaccard similarity
- Semantic similarity
Applications
- Document deduplication
- Content recommendation
- Plagiarism detection
- Information retrieval
Pattern Discovery
Collocation Detection
- Statistical measures
- Association rules
- Phrase mining
- N-gram analysis
Event Detection
- Temporal patterns
- Burst detection
- Topic evolution
- Trend analysis
Representation Learning
Auto-encoders
- Text auto-encoders
- Variational auto-encoders
- Sequence-to-sequence
- Reconstruction quality
Self-supervised Learning
- Masked language modeling
- Next sentence prediction
- Rotation prediction
- Contrastive learning
Best Practices
- Data preprocessing
- Model selection
- Evaluation strategies
- Hyperparameter tuning
- Result interpretation
Common Challenges
- High dimensionality
- Sparsity
- Scalability
- Interpretability
- Evaluation
Tools and Libraries
- Gensim
- scikit-learn
- SpaCy
- NLTK
- Transformers
Related Topics
- Text Preprocessing
- Dimensionality Reduction
- Evaluation Metrics
- Visualization Techniques