Information fusion for automatic text classification

摘要

Analysis and classification of free text documents encompass decision-making processes that rely on several clues derived from text and other contextual information. When using multiple clues, it is generally not known a priori how these should be integrated into a decision. An algorithmic sensor based on Latent Semantic Indexing (LSI) (a recent successful method for text retrieval rather than classification) is the primary sensor used in our work, but its utility is limited by the (ital reference)(ital library) of documents. Thus, there is an important need to complement or at least supplement this sensor. We have developed a system that uses a neural network to integrate the LSI-based sensor with other clues derived from the text. This approach allows for systematic fusion of several information sources in order to determine a combined best decision about the category to which a document belongs.