Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction
Patrick Hohenecker‚ Frank Mtumbuka‚ Vid Kocijan and Thomas Lukasiewicz
The goal of open information extraction (OIE) is to extract facts from natural language text, and to represent them as structured triples of the form (subject, predicate, object). For example, given the sentence »Beethoven composed the Ode to Joy.«, we are expected to extract the triple (Beethoven, composed, Ode to Joy). In this work, we systematically compare different neural network architectures and training approaches, and improve the performance of the currently best models on the OIE16 benchmark (Stanovsky and Dagan, 2016) by 0.421 F 1 score and 0.420 AUCPR, respectively, in our experiments (i.e., by more than 200% in both cases). Furthermore, we show that appropriate problem and loss formulations often affect the performance more than the network architecture.