This is a literature review on neural networks and related algorithms. It is aimed to get a general understanding on neural networks and find out the possible applications of these models in information retrieval (IR) systems.

Beginning with a preliminary definition and typical structure of neural networks, neural networks are studied with respect to their learning processes and architecture structures. A case study on some specific networks and related algorithms is followed. The applications of some neural network models and related algorithms in information retrieval systems are then investigated. Problems on applying neural network models into IR systems are finally summarized in the conclusion.

Neural network is one of the important components in Artificial Intelligence (AI). It has been studied for many years in the hope of achieving human-like performance in many fields, such as speech and image recognition as well as information retrieval. To make the term ‘neural network’ used in this paper clear and to expand considerably on its content, it is useful to give a definition to this term, analyze the general structure of a neural network, and explore the advantages of neural network models first.
In his book (1990), Miller have found that “Neural networks are also called neural nets, connectionist models, collective models, parallel distributed processing models, neuromorphic systems, and artificial neural networks by various researchers (p.1-4).” Similarly, in his article (1987), Lippmann states, “artificial neural net models or simple ‘neural nets’ go by many names such as connectionist models, parallel distributed processing models, and neuromorphic systems (p.4).” However, Doszkocs and his coworkers (1990) think connectionist models are more general than neural network models and “they include several related information processing approaches, such as artificial neural networks, spreading activation models, associative networks, and parallel distributed processing (p. 209).” In their mind, “early connectionist models were called neural network models because they literally tried to model networks of brain cells (neurons) (p. 212)”. A neural network model (or neural model) as that term is used refers to a connectionist model that simulates the biophysical information processing occurring in the nervous system. So, even though connectionist models and neural network models have same meaning in some literature, we prefer to regard connectionist models as a more general concept and neural networks is a subgroup of it.

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