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Relation Extraction Using TBL with Distant Supervision

Supervised machine learning methods have been widely used in relation extraction that finds the relation between two named entities in a sentence. However, their disadvantages are that constructing training data is a cost and time consuming job, and the machine learning system is dependent on the domain of the training data. To overcome these disadvantages, we construct a weakly labeled data set using distant supervision and propose a relation extraction system using a transformation-based learning (TBL) method. This model showed a high F1-measure (86.57%) for the test data collected using distant supervision but a low F1-measure (81.93%) for gold label, due to errors in the training data collected by the distant supervision method.