NLP & Text Mining
Extract meaning, structure, and signals from natural-language text.
NLP and text mining extract meaning, structure, and signals from natural-language text. The methods below span modern Transformer-based LLMs and retrieval through to fast classical baselines like TF-IDF and Naive Bayes for cheap, explainable classification.
- Use LLMs/Transformers for high-quality NLP.
- Use TF-IDF plus Logistic Regression/Linear SVM for cheap, fast, explainable text classification.
| # | Algorithm | Best for | Common fields |
|---|---|---|---|
| 1 | Transformer Models / LLMs | Most modern NLP tasks |
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| 2 | Embeddings + Vector Search | Semantic search and retrieval |
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| 3 | TF-IDF + Linear Models | Fast classical NLP baseline |
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| 4 | Naive Bayes | Simple text classification |
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| 5 | Topic Modeling: LDA, NMF | Discovering themes in text |
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| 6 | CRF / HMM | Sequence labeling, older NLP |
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| 7 | Word2Vec / GloVe / FastText | Static word embeddings |
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