Chandan Singh | info retrieval

info retrieval

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Some notes on information retrieval, based on UVA”s Info Retrieval course.


  • building blocks of search engines
    • search (user initiates)
    • reccomendations - proactive search engine (program initiates e.g. pandora, netflix)
    • information retrieval - activity of obtaining info relevant to an information need from a collection of resources
    • information overload - too much information to process
    • memex - device which stores records so it can be consulted with exceeding speed and flexibility (search engine)
  • IR pieces
    1. Indexed corpus (static)
      • crawler and indexer - gathers the info constantly, takes the whole internet as input and outputs some representation of the document
        • web crawler - automatic program that systematically browses web
      • document analyzer - knows which section has what -takes in the metadata and outputs the index (condensed), manage content to provide efficient access of web documents
    2. User
      • query parser - parses the search terms into managed system representation
    3. Ranking
      • ranking model -takes in the query representation and the indices, sorts according to relevance, outputs the results
      • also need nice display
      • query logs - record user’s search history
      • user modeling - assess user’s satisfaction
  • steps
    1. repository -> document representation
    2. query -> query representation
    3. ranking is performed between the 2 representations and given to the user
    4. evaluation - by users
  • information retrieval:
    1. reccomendation
    2. question answering
    3. text mining
    4. online advertisement

related fields

they are all getting closer, database approximate search and information extraction converts unstructed data to structured:

database systems information retrieval
structured data unstructured data
semantics are well-defined semantics are subjective
structured query languages (ex. SQL) simple keyword queries
exact retrieval relevance-drive retrieval
emphasis on efficiency emphasis on effectiveness
  • natural language processing - currently the bottleneck
    • deep understainding of language
    • cognitive approaches vs. statistical
    • small scale problems vs. large
  • developing areas
    • currently mobile search is big - needs to use less data, everything needs to be more summarized
    • interactive retrieval - like a human being, should collaborate
  • core concepts
    • information need - desire to locate and obtain info to satisfy a need
    • query - a designed representation of user’s need
    • document - representation of info that could satisfy need
    • relevance - relatedness between documents and need, this is vague
      • multiple perspectives: topical, semantic, temporal, spatial (ex. gas stations shouldn’t be behind you)
  • Yahoo used to have system where you browsed based on structure (browsing), but didn’t have queries (querying)
    • better when user doesn’t know keywords, just wants to explore
    • push mode - systems push relevant info to users without a query
    • pull mode - users pull out info using keywords