SYSTEMS AND METHODS FOR INITIAL LEARNING OF AN ADAPTIVE DETERMINISTIC CLASSIFIER FOR DATA EXTRACTION

摘要

This disclosure relates to initial learning of a classifier for automating extraction of structured data from unstructured or semi-structured data. In one embodiment, a method is disclosed, comprising: identifying at least one expected relation class associated with at least one expected relation data; populating at least one expected name entity data from the at least one identified expected relation class; generating training data by tagging the at least one expected relation data and the at least one identified expected relation class with unstructured or semi-structured data; generating feedback data for a relation data and relation class, using a convergence technique on the tagged training data; retuning a NE classifier cluster and a relation classifier cluster by continuously tagging new training data or generating new cascaded expression for a deterministic classifier and a statistical classifier; and extracting the structured data when the NE classifier cluster and the relation classifier cluster converge.