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Expanding Query Terms in Context

• Chris Staff and Robert Muscat
• Department of Computer Science AI
• University of Malta

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Aims of this presentation

• Background
• The Vocabulary Problem in IR
• Scenario
• Using retrieved documents to determine how to
  expand query
• Approach
• Evaluation

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The Vocabulary Problem

• Furnas et al, 1987, find that any two people
  describe the same concept/object using the same
  term with a probability of less than .2
• This is a huge problem for IR
• High probability of finding some documents about
  your term (but watch ambiguous terms!)
• Low probability of finding all documents about
  your concept (so low coverage)

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Whats Query Expansion?

• Adding terms to query to improve recall while
  keeping precision high
• Recall is 1 when all relevant docs are retrieved
• Precision is 1 when all retrieved docs are
  relevant

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Whats Query Expansion?

• Attempts to improve recall (adding synonyms)
  usually involve constructed thesaurus (Qiu et al,
  1995, Mandala et al, 1999, Voorhees, 1994)
• Attempts to improve precision (by adding
  restricting terms) now based around automatic
  relevance feedback (e.g., Mitra et al, 1998)
• Indiscriminate query expansion can lead to loss
  of precision (Voorhees, 1994) or hurt recall

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Scenario

• Two users search for information related to the
  same concept C
• User queries Q1 and Q2 have no terms in common
• R1 and R2 are results sets of Q1 and Q2
  respectively
• Rcommon R1 ? R2

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Scenario

• We assume that Rcommon is small and non-empty
  (Furnas, 1985 and Furnas et al, 1987)
• If Rcommon is large then Q1 and Q2 will both
  retrieve same set of documents
• Can determine (using WordNet) if any term in Q1
  is the synonym of a term in Q2
• Some doc Dk in Rcommon probably includes both
  terms (because of way Web IR works)!

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Scenario

• If t1 in Q1 and t2 in Q2 are synonyms
• Can expand either in future queries containing t1
  or t2
• As long as doc Dk appears in results set (the
  context)

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Approach

• Learning synonyms in context
• Query Expansion

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Learning Synonyms in Context

• A document is associated with a bag of words
  ever used to retrieve doc
• A term, document pair is associated with a synset
  for the term in the context of the doc
• Word sense from WordNet also recorded to reduce
  ambiguity

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Query Expansion in Context

• Submit unexpanded original user query Q to obtain
  results set R
• For each document Dk in R (k is rank) retrieve
  synsets for terms in Q
• Same query term in context of different docs in R
  may yield inconsistent synsets
• Countered using Inverse Document Relevance

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Inverse Document Relevance

• IDR is relative frequency with which doc d is
  retrieved in rank k when term q occurs in the
  query
• IDRq,d Wq,d / Wd (where Wd is number of times d
  retrieved, Wq,d number of times d retrieved when
  q occurs in query)

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Term Document Relevance

• We then re-rank documents in R based on their TDR
• TDRq,d,k IDRq,d x Wq,d,k / Wd,k
• Synsets of top-10 re-ranked document are merged
  according to word category and sense
• Most frequently occurring word category, word
  sense pair synset used to expand q in query

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Evaluation

• Need huge query log, ideally, with relevance
  judgements for queries
• We have TREC QA collection, but well need to
  index them before running the test queries
  through them (using, e.g., SMART)
• Disadvantage that there might not be enough
  queries
• User Studies

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• Thank you!