Paper accepted at EMNLP: MinIE: Minimizing Facts in Open Information Extraction

The paper "MinIE: Minimizing Facts in Open Information Extraction (pdf) by K. Gashteovski, L. del Corro, and R. Gemulla has been accepted for the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP).

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 MinIE, an OIE system that aims to provide useful, compact extractions with high precision and recall. MinIE 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 MinIE achieves competitive or higher precision and recall than most prior systems, while at the same time producing shorter, semantically enriched extractions.