Utilizing phrase based semantic information for term dependency

ABSTRACT

Previous work on term dependency has not taken into account semantic information underlying query phrases. In this work, we study the impact of utilizing phrase based concepts for term dependency. We use Wikipedia to separate important and less important term dependencies, and treat them accordingly as features in a linear feature-based retrieval model. We compare our method with a Markov Random Field (MRF) model on four TREC document collections. Our experimental results show that utilizing phrase based concepts improves the retrieval effectiveness of term dependency, and reduces the size of the feature set to large extent.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

General Terms:
Experimentation, Performance

Keywords:
Markov random field, Wikipedia, information retrieval, phrase, term dependency