Building Relation Extraction Templates via Unsupervised Learning

Abstract

The vast amount of text published daily over the internet pose an opportunity to build unsupervised text mining models with a better or a comparable performance than existing models. In this paper, we investigate the problem of relation extraction and generation from text using an unsupervised model learned from news published online. We propose a clustering-based method to build a dataset of relations examples. News articles are clustered and once a cluster of sentences for each event in each piece of news is formed, relations between important entities in each event cluster are extracted and considered as examples of relations. Relations examples are used to build extraction templates in order to extract and generate readable relations summaries from new instances of news. The proposed unsupervised relation extraction and generation method is evaluated against multiple methods for relation extraction over different datasets where the proposed method has shown a comparable performance.