Query modification based on relevance back-propagation in an ad hoc environment Export

@article{citeulike:2077192, abstract = {It is well-known that relevance feedback is a method significant in improving the effectiveness of information retrieval systems. Improving effectiveness is important since these information retrieval systems must gain access to large document collections distributed over different distant sites. As a consequence, efforts to retrieve relevant documents have become significantly greater. Relevance feedback can be viewed as an aid to the information retrieval task. In this paper, a relevance feedback strategy is presented. The strategy is based on back-propagation of the relevance of retrieved documents using an algorithm developed in a neural approach. This paper describes a neural information retrieval model and emphasizes the results obtained with the associated relevance back-propagation algorithm in three different environments: manual ad hoc, automatic ad hoc and mixed ad hoc strategy (automatic plus manual ad hoc).}, author = {Boughanem, M. and Chrisment, C. and Soule-Dupuy, C.}, citeulike-article-id = {2077192}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0306-4573(99)00008-4}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6VC8-3WH644G-2/2/4d0b2cea83482a81c05020062c678f3f}, doi = {10.1016/S0306-4573(99)00008-4}, journal = {Information Processing \& Management}, keywords = {activation, back-propagation, connectionist, feedback, mercure, model, relevance, spreading}, month = {March}, number = {2}, pages = {121--139}, posted-at = {2007-12-08 10:44:48}, priority = {3}, title = {Query modification based on relevance back-propagation in an ad hoc environment}, url = {http://dx.doi.org/10.1016/S0306-4573(99)00008-4}, volume = {35}, year = {1999} }

TY - JOUR ID - citeulike:2077192 L3 - citeulike-article-id:2077192 N2 - It is well-known that relevance feedback is a method significant in improving the effectiveness of information retrieval systems. Improving effectiveness is important since these information retrieval systems must gain access to large document collections distributed over different distant sites. As a consequence, efforts to retrieve relevant documents have become significantly greater. Relevance feedback can be viewed as an aid to the information retrieval task. In this paper, a relevance feedback strategy is presented. The strategy is based on back-propagation of the relevance of retrieved documents using an algorithm developed in a neural approach. This paper describes a neural information retrieval model and emphasizes the results obtained with the associated relevance back-propagation algorithm in three different environments: manual ad hoc, automatic ad hoc and mixed ad hoc strategy (automatic plus manual ad hoc). IS - 2 JF - Information Processing & Management EP - 139 TI - Query modification based on relevance back-propagation in an ad hoc environment VL - 35 SP - 121 KW - activation KW - back-propagation KW - connectionist KW - feedback KW - mercure KW - model KW - relevance KW - spreading AU - Boughanem, M AU - Chrisment, C AU - Soule-Dupuy, C PY - 1999/03// UR - http://dx.doi.org/10.1016/S0306-4573(99)00008-4 ER -