MinlE: Minimizing Facts in Open Information Extraction

摘要

The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner. In this paper, we propose MinlE, an OIE system that aims to provide useful, compact extractions with high precision and recall. MinlE approaches these goals by (1) representing information about polarity, modality, attribution, and quantities with semantic annotations instead of in the actual extraction, and (2) identifying and removing parts that are considered overly specific. We conducted an experimental study with several real-world datasets and found that MinlE achieves competitive or higher precision and recall than most prior systems, while at the same time producing shorter, seman-tically enriched extractions.