Literature Text Categorization

[azhe825]

• Machine learning in automated text categorization
• A comparative study on feature selection in text categorization
• A re-examination of text categorization methods
• Supervised and Semi-Supervised Text Categorization using One-Hot LSTM for Region Embeddings
• Hybrid feature selection based on enhanced genetic algorithm for text categorization
• Multi-label Text Categorization Using L_ {21}-norm Minimization Extreme Learning Machine

[azhe825]

• Machine learning in automated text categorization

DPC: document i belongs to which categories, new documents may come in. CPC: category j contains which documents, new category may be added.

Output: hard vs. ranking. Ranking is especially useful when a human expert make the final decision.

Application:

1. Boolean systems (document indexing, put key words on a document) fixed vocabulary of key words. Multi-label, multi-classification
2. Document Organization. single-label, multi-classification
3. Text Filtering. binary-classification
4. Word Sense Disambiguation. Context related.
5. Hierarchical Categorization of Web Pages. Ranking, CPC, links are useful.

DIA: more features. tf+idf, locations of term j in document i.

Dimensionality Reduction:

1. df
2. supervised. probability. P(C|t)=P(t|C)_P(C)/(P(t|C)_P(C)+P(t|C-)*P(C-))
3. term clustering,
4. LSI. not interpretable

Document similarity: cosine distance

Online: perceptron, WINNOW

Evaluation: Microaveraging mu Macroaveraging M (for precision and recall)