AUTOMATIC EXTRACTION OF A TRAINING CORPUS FOR A DATA CLASSIFIER BASED ON MACHINE LEARNING ALGORITHMS

摘要

An iterative classifier for unsegmented electronic documents is based on machine learning algorithms. The textual strings in the electronic document are segmented using a composite dictionary that combines a conventional dictionary and an adaptive dictionary developed based on the context and nature of the electronic document. The classifier is built using a corpus of training and testing samples automatically extracted from the electronic document by detecting signatures for a set of pre-established classes for the textual strings. The classifier is further iteratively improved by automatically expanding the corpus of training and testing samples in real-time when textual strings in new electronic documents are processed and classified.