@article{Lillis2006d, title = {Probability-{{Based Fusion}} of {{Information Retrieval Result Sets}}}, author = {Lillis, David and Toolan, Fergus and Mur, Angel and Peng, Liu and Collier, Rem and Dunnion, John}, year = {2006}, month = {November}, journal = {Artificial Intelligence Review}, volume = {25}, number = {1-2}, pages = {179--191}, issn = {0269-2821}, doi = {10.1007/s10462-007-9021-x}, url = {http://www.springerlink.com/content/q257h56314612mn5}, abstract = {Information Retrieval (IR) forms the basis of many information management tasks. Information management itself has become an extremely important area as the amount of electronically available information increases dramatically. There are numerous methods of performing the IR task both by utilising different techniques and through using different representations of the information available to us. It has been shown that some algorithms outperform others on certain tasks. Very little progress has been made in fusing various techniques to improve the overall retrieval performance of a system. This paper introduces a probability-based fusion technique probFuse that shows initial promise in addressing this question. It also compares probFuse with the common CombMNZ data fusion technique.}, }