Examples using the Dragon Toolkit

• MaxMatcher: Biolgoical Term Extraction
  
  
• Text Retrieval Using Ontology: Topic Signature Language Models for text retrieval. UMLS-based Concept and Concept Pairs are used as topic signatures.
  
  
• Xtract: Build a multiword phrase dictionary from a collection.
  
  
• Text Retrieval Using Multiword Phrase: Topic Signature Language Models for text retrieval. Multiword phrases are used as topic signatures.
  
  
• Text Clustering: Agglomerative clustering, spherical k-means, and model-based k-means with four smoothing approaches (Laplacian smoothing, background smoothing, context sensitive semantic smoothing, and context insensitive semantic smoothing)
  
  
• Link K-Means:Utilize both content and hyper linkages between documents for clustering
  
  
• Text Classification: SVM classifier, Nigam active learning classifier, and Bayesian classifiers with four smoothing approaches (Laplacian smoothing, background smoothing, context sensitive semantic smoothing, and context insensitive semantic smoothing)
  
  
• Text Summarization: LexRank Generic Multi-Document Summarization
  
  
• Topic Modeling: LDA, Aspect Model as well as Simple Mixture Model

• MaxMatcher: Biolgoical Term Extraction
  
  
• Indexing: Index a collection using Basic Token Indexer.
  
  
• Text Process: Tokenize, lemmatise and part-of-speech tag.