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Found 4 papers, 2 papers with code
Systematic Comparison of Neural Architectures and Training Approaches for Open Information Extraction
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 {\textless}subject, predicate, object{\textgreater}.
Controlling Text Edition by Changing Answers of Specific Questions
Experimental results on the test set show that our proposed method is a good fit for this novel NLP task.
Ontology Reasoning with Deep Neural Networks
This is an important and at the same time very natural logical reasoning task, which is why the presented approach is applicable to a plethora of important real-world problems.
Deep Learning for Ontology Reasoning
In this work, we present a novel approach to ontology reasoning that is based on deep learning rather than logic-based formal reasoning.
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