Vocabulary mismatch

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Vocabulary mismatch is a common phenomenon in the usage of natural languages, occurring when different people name the same thing or concept differently.

Furnas et al. (1987) were perhaps the first to quantitatively study the vocabulary mismatch problem.[1] Their results show that on average 80% of the times different people (experts in the same field) will name the same thing differently. There are usually tens of possible names that can be attributed to the same thing. This research motivated the work on latent semantic indexing.

The vocabulary mismatch between user created queries and relevant documents in a corpus causes the term mismatch problem in information retrieval. Zhao and Callan (2010)[2] were perhaps the first to quantitatively study the vocabulary mismatch problem in a retrieval setting. Their results show that an average query term fails to appear in 30-40% of the documents that are relevant to the user query. They also showed that this probability of mismatch is a central probability in one of the fundamental probabilistic retrieval models, the Binary Independence Model. They developed novel term weight prediction methods that can lead to potentially 50-80% accuracy gains in retrieval over strong keyword retrieval models. Further research along the line shows that expert users can use Boolean Conjunctive Normal Form expansion to improve retrieval performance by 50-300% over unexpanded keyword queries.[3]

Techniques that solve mismatch[edit]

Zhao provided a survey of common techniques that can solve mismatch in the dissertation on term mismatch.[4]

Stemming[edit]

Full-text indexing versus only indexing keywords or abstracts[edit]

Usages of inlink anchor text or other social tagging[edit]

Query expansion[edit]

A recent study by Zhao and Callan (2012)[3] using expert created manual Conjunctive normal form queries has shown that searchonym expansion in the Boolean conjunctive normal form is much more effective than the traditional bag of word expansion e.g. Rocchio expansion.

A wiki website called WikiQuery has been developed by one of the authors of the above study, which helps users create, store and share effective Conjunctive normal form queries.

Translation based models[edit]

References[edit]

  1. ^ Furnas, G., et al, The Vocabulary Problem in Human-System Communication, Communications of the ACM, 1987, 30(11), pp. 964-971.
  2. ^ Zhao, L. and Callan, J., Term Necessity Prediction, Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM 2010). Toronto, Canada, 2010.
  3. ^ a b Zhao, L. and Callan, J., Automatic term mismatch diagnosis for selective query expansion, SIGIR 2012.
  4. ^ Zhao, L., Modeling and Solving Term Mismatch in Full-text Retrieval, PhD Dissertation, Carnegie Mellon University, 2012. URL retrieved 9/3/2012.

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