Improving Open Information Extraction with Distant Supervision Learning

Open information extraction (Open IE), as one of the essential applications in the area of Natural Language Processing (NLP), has gained great attention in recent years. As a critical technology for building Knowledge Bases (KBs), it converts unstructured natural language sentences into structured representations, usually expressed in the form of triples. Most conventional open information extraction approaches leverage a series of manual pre-defined extraction patterns or learn patterns from labeled training examples, which requires a large number of human resources. Additionally, many Natural Language Processing tools are involved, which leads to error accumulation and propagation. With the rapid development of neural networks, neural-based models can minimize the error propagation problem, but it also faces the problem of data-hungry in supervised learning. Especially, they leverage existing Open IE tools to generate training data, and it causes data quality issues. In this paper, we employ a distant supervision learning approach to improve the Open IE task. We conduct extensive experiments by employing two popular sequence-to-sequence models (RNN and Transformer) and a large benchmark data set to demonstrate the performance of our approach.