Method and system for identifying labels of unlabelled column data

Abstract

As discussed earlier, labelling techniques that are available for labelling of unlabelled tabular data use some semi supervised models for identification purposes. However, they require sample labeled data for training purposes. Further, the same labelling model/technique cannot be used for all data types. Present disclosure provides method and system for identifying labels of unlabeled column data. The system uses a hybrid approach i.e., it uses language models, regular expressions and known dictionaries for labelling of unlabelled tabular data. For performing labelling, system first classifies received unlabelled tabular data into one or more data buckets. The system then uses appropriate techniques, based on data types, for identification of labels of unlabeled data present in data buckets. Thereafter, system uses feedback mechanism which will impart maturity to system over time. Finally, once system is matured, system can identify labels for all types of data.