A probabilistic learning approach for document indexing
- 1 July 1991
- journal article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Information Systems
- Vol. 9 (3), 223-248
- https://doi.org/10.1145/125187.125189
Abstract
We describe a method for probabilistic document indexing using relevance feedback datathat has been collected from a set of queries. Our approach is based on three new concepts:(1) Abstraction from specific terms and documents, which overcomes the restriction of limitedrelevance information for parameter estimation. (2) Flexibility of the representation, whichallows the integration of new text analysis and knowledge-based methods in our approach aswell as the consideration of document ...Keywords
This publication has 17 references indexed in Scilit:
- Probabilistic approaches to the document retrieval problemPublished by Springer Nature ,2005
- SILOL: A simple logical-linguistic document retrieval systemInformation Processing & Management, 1990
- Optimum polynomial retrieval functions based on the probability ranking principleACM Transactions on Information Systems, 1989
- The effectiveness of a nonsyntactic approach to automatic phrase indexing for document retrievalJournal of the American Society for Information Science, 1989
- Probabilistic and genetic algorithms in document retrievalCommunications of the ACM, 1988
- Document representation in probabilistic models of information retrievalJournal of the American Society for Information Science, 1981
- A THEORETICAL BASIS FOR THE USE OF CO‐OCCURRENCE DATA IN INFORMATION RETRIEVALJournal of Documentation, 1977
- Relevance weighting of search termsJournal of the American Society for Information Science, 1976
- Precision Weighting—An Effective Automatic Indexing MethodJournal of the ACM, 1976
- On Relevance, Probabilistic Indexing and Information RetrievalJournal of the ACM, 1960