Basic Workflows/Algorithms for machine Learning in Text Processing

Web Extraction:

•  Apache Tika (or Any Custom Crawler)

Concept Extraction

•     Sentence Detection (maxent-3.0.0.jar, opennlp-tools-1.5.3.jar)
•     NER(Named Entity Recognition)
•     Concept Extraction (snowball-stemmer-1.3.0.581.1.jar)
•     Multi phrase to list phrase

Concept Filtering

•   Zipf Filtering
•   ChiSq Filtering
•   Low frequency Filtering
•   Signal Filtering

String Indexer:

•    StringIndexer (spark-mlib_2.10-2.1.0.jar)

Hashing TF

•    HashingTF (spark-mlib_2.10-2.1.0.jar)
•    IDF (spark-mlib_2.10-2.1.0.jar)

Classifier for Supervised Classification Algorithms:  [Train & Predict]

• . Naive Bayes 
• . Support Vector Machines (C Value, Gamma Value & Karnel)
• . Decision Trees  (Entropy, Min-Sample-Slits, Impurity, Information Gain)
• . K Nearest Neighbors
• . Random Forest
• . Linear Regression/Logistic Regression
• . Adaboost (Sometimes Also Called Boosted Decision Tree)

UnSupervised Classification algorithms 

• Clustering/SubClustering (Scoring)
  • k-means
  • Density based Spatial clustering (DBSCAN) (SubClustering Algorithm)
  • Mixture models
  • Hierarchical clustering
• anomaly detection(OutLier's Detection)
• Neural Networks
  • Hebbian Learning
  • Generative Adversarial Networks