Keywords: Sentence encoding, Relation extraction, Unsupervised
TL;DR: We present a novel method that uses sentence encoding for unsupervised relation extraction.
Abstract: Relation extraction between two named entities from unstructured text is an important natural language processing task. In the absence of labelled data, semi-supervised and unsupervised approaches are used to extract relations. We present a novel method that uses sentence encoding for unsupervised relation extraction. We use a pre-trained, S-BERT based model for sentence encoding. The model classifies identical patterns using a clustering algorithm. These patterns are used to extract relations between two named entities in a given text. The system calculates a confidence value above a certain threshold to avoid semantic drift. The experimental results show that without any explicit feature selection and independent of the size of the corpus, our system achieves better F score than state-of-the-art unsupervised models.