Retrieval test evaluation of a rule based automatic indexing (AIR/PHYS)
Proc. of the third joint BCS and ACM symposium on Research and development in information retrieval
Approaches to intelligent information retrieval
Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval
Linear structure in information retrieval
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Optimum probability estimation based on expectations
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
The automatic indexing system AIR/PHYS - from research to applications
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
Two models of retrieval with probabilistic indexing
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
A decision theory approach to optimal automatic indexing
SIGIR '82 Proceedings of the 5th annual ACM conference on Research and development in information retrieval
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Probabilistic document indexing from relevance feedback data
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
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We show that any approach to develop optimum retrieval functions is based on two kinds of assumptions: first, a certain form of representation for documents and requests, and second, additional simplifying assumptions that predefine the type of the retrieval function. Then we describe an approach for the development of optimum polynomial retrieval functions: request-document pairs (ƒl, dm) are mapped onto description vectors @@@@(ƒl, dm), and a polynomial function of the form @@@@T · @@@@(@@@@) is developed such that it yields estimates of the probability of relevance P(R|@@@@(ƒl, dm)) with minimum square errors. We give experimental results for the application of this approach to documents with weighted indexing as well as to documents with complex representations. In contrast to other probabilistic models, our approach yields estimates of the actual probabilities, it can handle very complex representations of documents and requests, and it can be easily applied to multi-valued relevance scales. On the other hand, this approach is not suited to log-linear probabilistic models, and it needs large samples of relevance feedback data for its application.