TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Mining term association patterns from search logs for effective query reformulation
Proceedings of the 17th ACM conference on Information and knowledge management
Patterns of query reformulation during Web searching
Journal of the American Society for Information Science and Technology
Query reformulation using anchor text
Proceedings of the third ACM international conference on Web search and data mining
Utilizing sub-topical structure of documents for information retrieval
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
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We propose a generative model for automatic query reformulations from an initial query using the underlying subtopic structure of top ranked retrieved documents. We address three types of query reformulations: a) specialization; b) generalization; and c) drift. To test our model we generate three reformulation variants starting with selected fields from the TREC-8 topics as the initial queries. We use manual judgements from multiple assessors to measure the accuracy of the reformulated query variants and observe accuracies of 65%, 82% and 69% respectively for specialization, generalization and drift reformulations.