Unit RR, Part A: Features

Feature = Word in Description

• flat, uniform, simple system
• assume term independence
• assume large but finite set
• compute weights based on frequency in description
• compute weights based also on frequency in collection

Example: Student Description

• case 1: feature - word in description
• case 2: subvectors of features
  • group features by type or by class
  • typical groupings: classroom, course, professor, residence, sport
  • process sub-vectors separately
  • combine sub-vector results
  • sub-vectors probably are not independent

General Issues

• How many? Which to use? (query expansion)
  • use a small number
  • use ones closely related to the original term (query)
• How to assign weights? (term weighting)
  • in collection (e.g., IDF, DV, noise)
  • in document (e.g., TF --- or normalize and scale for collection)
  • combination (e.g., product)

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Alternatives for Feature Selection

• Word
• Stem or root or non-plural form
• Thesaurus category
• Descriptor
• N-gram (e.g., bigram, trigram)
• Factor from SVD in LSI scheme
• Attribute (value pair)