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Previous studies in Open Information Extraction (Open IE) are mainly based onextraction patterns. They manually define patterns or automatically learn themfrom a large corpus. However, these approaches are limited when grasping thecontext of a sentence, and they fail to capture implicit relations. In thispaper, we address this problem with the following methods. First, we exploitlong short-term memory (LSTM) networks to extract higher-level features alongthe shortest dependency paths, connecting headwords of relations and arguments.The path-level features from LSTM networks provide useful clues regardingcontextual information and the validity of arguments. Second, we constructedsamples to train LSTM networks without the need for manual labeling. Inparticular, feedback negative sampling picks highly negative samples amongnon-positive samples through a model trained with positive samples. Theexperimental results show that our approach produces more precise and abundantextractions than state-of-the-art open IE systems. To the best of ourknowledge, this is the first work to apply deep learning to Open IE.

Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling