- author = "Ronan Cummins and Colm O'Riordan",
- title = "Learning in a pairwise term-term proximity framework for information retrieval",
- booktitle = "SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval",
- year = "2009",
- editor = "James Allan and Javed Aslam",
- pages = "251--258",
- address = "Boston, MA, USA",
- publisher_address = "New York, NY, USA",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, information retrieval, learning to rank, proximity",
- isbn13 = "978-1-60558-483-6",
-
DOI = "
doi:10.1145/1571941.1571986",
-
abstract = "Traditional ad hoc retrieval models do not take into
account the closeness or proximity of terms. Document
scores in these models are primarily based on the
occurrences or non-occurrences of query-terms
considered independently of each other. Intuitively,
documents in which query-terms occur closer together
should be ranked higher than documents in which the
query-terms appear far apart.
This paper outlines several term-term proximity measures and develops an intuitive framework in which they can be used to fully model the proximity of all query-terms for a particular topic. As useful proximity functions may be constructed from many proximity measures, we use a learning approach to combine proximity measures to develop a useful proximity function in the framework. An evaluation of the best proximity functions show that there is a significant improvement over the baseline ad hoc retrieval model and over other more recent methods that employ the use of single proximity measures.",
- notes = "Also known as \cite{1571986}",
