Distant Supervision for Relation Extraction in The Persian Language using Piecewise Convolutional Neural Networks

Abstract

In this paper we use distant supervision for the task of relation extraction from a large corpus in the Persian language. There are supervised and unsupervised methods for relation extraction from text. In supervised relation extraction we use hand labeled corpora. This method suffers from domain dependencies and the difficulties of labeling the text. In unsupervised method, we use large corpora without having to label them but relations extracted using this method cannot be used to populate knowledge bases. Distant supervision takes advantage of large corpora without suffering from domain dependencies and can populate knowledge bases. For our experiment we use FarsBase, a knowledge base containing millions of relation instances, and align entities in 630000 Persian Wikipedia articles to these relation instances and create a distantly supervised dataset. We then extract new relation instances using piecewise convolutional neural networks and compare the results with the baseline model that uses manually extracted features.