A Bayesian Network Model for Information Retrieval from Greek Texts

The present paper describes a Bayesian network approach to information retrieval (IR) from natural language texts in Greek. The network structure provides an intuitive representation of uncertainty relationships and the embedded conditional probability table is used by inference algorithms in an attempt to identify documents that are relevant to the user's needs, expressed in the form of Boolean queries. Our research has been directed in constructing a probabilistic IR framework that focus on assisting users perform ad-hoc retrieval of Greek documents from the domain of economics. Furthermore, users can integrate feedback regarding the relevance of the retrieved documents in an attempt to improve performance on upcoming requests. Towards these goals, we have developed the Bayesian network IR system and tested it on several web corpora with different application domains. We have developed two different approaches with regard to the structure: a simple one, where the structure is manually provided, and an automated one, where data mining is used in order to extract the network's structure. Results have depicted satisfactory performance in terms of precision-recall curves.