Adaptive information retrieval: using a connectionist representation to retrieve and learn about documents

AIR represents a connectionist approach to the task of information retrieval. The system uses relevance feedback from its users to change its representation of authors, index terms and documents so that, over time, AIR improves at its task. The result is a representation of the consensual meaning of keywords and documents shared by some group of users. The central focus goal of this paper is to use our experience with AIR to highlight those characteristics of connectionist representations that make them particularly appropriate for IR applications. We argue that this associative representation is a natural generalization of traditional IR techniques, and that connectionist learning techniques are effective in this setting.

[1]  Gerard Salton,et al.  On the use of spreading activation methods in automatic information , 1988, SIGIR '88.

[2]  Lauren B. Doyle,et al.  Semantic Road Maps for Literature Searchers , 1961, JACM.

[3]  Lauren B. Doyle,et al.  Indexing and abstracting by association , 1962 .

[4]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[5]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[6]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[7]  Gerard Salton,et al.  Parallel text search methods , 1988, CACM.

[8]  Daniel E. Rose,et al.  Legal information retrieval a hybrid approach , 1989, ICAIL '89.

[9]  Michael C. Mozer,et al.  Inductive Information Retrieval Using Parallel Distributed Computation. , 1984 .

[10]  Geoffrey E. Hinton.  Connectionist Learning Procedures , 1989, Artif. Intell..

[11]  Paul R. Cohen,et al.  Information retrieval by constrained spreading activation in semantic networks , 1987, Inf. Process. Manag..

[12]  J J Hopfield,et al.  Collective computation in neuronlike circuits. , 1987, Scientific American.

[13]  H. Edmund Stiles,et al.  The Association Factor in Information Retrieval , 1961, JACM.

[14]  Craig Stanfill,et al.  Parallel free-text search on the connection machine system , 1986, CACM.

[15]  Deborah L. McGuinness,et al.  Knowledge representation, connectionism and conceptual retrieval , 1988, SIGIR '88.

[16]  Scott E. Fahlman,et al.  NETL: A System for Representing and Using Real-World Knowledge , 1979, CL.

[17]  Scott Everett Preece.  A spreading activation network model for information retrieval , 1981 .

[18]  Richard Kuehn Belew,et al.  Adaptive information retrieval: machine learning in associative networks (connectionist, free-text, browsing, feedback) , 1986 .

[19]  Geoffrey E. Hinton,et al.  Parallel Models of Associative Memory , 1989 .

[20]  Stephen F. Weiss,et al.  A relevance feedback system based on document transformations , 1967 .

[21]  Harold S. Stone,et al.  Parallel Querying of Large Databases: A Case Study , 1987, Computer.