  Previous studies in Open Information Extraction (Open IE) are mainly based on
extraction patterns. They manually define patterns or automatically learn them
from a large corpus. However, these approaches are limited when grasping the
context of a sentence, and they fail to capture implicit relations. In this
paper, we address this problem with the following methods. First, we exploit
long short-term memory (LSTM) networks to extract higher-level features along
the shortest dependency paths, connecting headwords of relations and arguments.
The path-level features from LSTM networks provide useful clues regarding
contextual information and the validity of arguments. Second, we constructed
samples to train LSTM networks without the need for manual labeling. In
particular, feedback negative sampling picks highly negative samples among
non-positive samples through a model trained with positive samples. The
experimental results show that our approach produces more precise and abundant
extractions than state-of-the-art open IE systems. To the best of our
knowledge, this is the first work to apply deep learning to Open IE.
