  Information extraction (IE) aims to produce structured information from an
input text, e.g., Named Entity Recognition and Relation Extraction. Various
attempts have been proposed for IE via feature engineering or deep learning.
However, most of them fail to associate the complex relationships inherent in
the task itself, which has proven to be especially crucial. For example, the
relation between 2 entities is highly dependent on their entity types. These
dependencies can be regarded as complex constraints that can be efficiently
expressed as logical rules. To combine such logic reasoning capabilities with
learning capabilities of deep neural networks, we propose to integrate logical
knowledge in the form of first-order logic into a deep learning system, which
can be trained jointly in an end-to-end manner. The integrated framework is
able to enhance neural outputs with knowledge regularization via logic rules,
and at the same time update the weights of logic rules to comply with the
characteristics of the training data. We demonstrate the effectiveness and
generalization of the proposed model on multiple IE tasks.
