ML Anthology

Learning Collection Fusion Strategies for Information Retrieval

Geoffrey G. Towell, Ellen M. Voorhees, Narendra Kumar Gupta, Ben Johnson-Laird
ICML 1995 pp. 540-548
doi:10.1016/B978-1-55860-377-6.50073-6 /icml/1995/towell1995icml-learning/

Abstract

In this paper we describe an Information Retrieval problem called collection fusion. The collection fusion problem is to maximize the number of relevant natural language documents retrieved given: a natural language query, multiple collections of documents, and a fixed total number of documents to retrieve. We describe two algorithms that use past queries to learn collection fusion strategies. Tests of these algorithms on a corpus of 742,000 documents indicate that they can learn good fusion strategies. Moreover, the strategies learned by our methods are consistently superior to those learned by a standard learning algorithm.

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Cite

Text

Towell et al. "Learning Collection Fusion Strategies for Information Retrieval." International Conference on Machine Learning, 1995. doi:10.1016/B978-1-55860-377-6.50073-6

Markdown

[Towell et al. "Learning Collection Fusion Strategies for Information Retrieval." International Conference on Machine Learning, 1995.](https://mlanthology.org/icml/1995/towell1995icml-learning/) doi:10.1016/B978-1-55860-377-6.50073-6

BibTeX

@inproceedings{towell1995icml-learning,
  title     = {{Learning Collection Fusion Strategies for Information Retrieval}},
  author    = {Towell, Geoffrey G. and Voorhees, Ellen M. and Gupta, Narendra Kumar and Johnson-Laird, Ben},
  booktitle = {International Conference on Machine Learning},
  year      = {1995},
  pages     = {540-548},
  doi       = {10.1016/B978-1-55860-377-6.50073-6},
  url       = {https://mlanthology.org/icml/1995/towell1995icml-learning/}
}