Making the most of limited training data using distant supervision

Abstract

Automatic recognition of relationships between key entities in text is an important problem which has many applications. Supervised machine learning techniques have proved to be the most effective approach to this problem. However, they require labelled training data which may not be available in sufficient quantity (or at all) and is expensive to produce. This paper proposes a technique that can be applied when only limited training data is available. The approach uses a form of distant supervision but does not require an external knowledge base. Instead, it uses information from the training set to acquire new labelled data and combines it with manually labelled data. The approach was tested on an adverse drug data set using a limited amount of manually labelled training data and shown to outperform a supervised approach.