  This paper presents a practical approach to fine-grained information
extraction. Through plenty of experiences of authors in practically applying
information extraction to business process automation, there can be found a
couple of fundamental technical challenges: (i) the availability of labeled
data is usually limited and (ii) highly detailed classification is required.
The main idea of our proposal is to leverage the concept of transfer learning,
which is to reuse the pre-trained model of deep neural networks, with a
combination of common statistical classifiers to determine the class of each
extracted term. To do that, we first exploit BERT to deal with the limitation
of training data in real scenarios, then stack BERT with Convolutional Neural
Networks to learn hidden representation for classification. To validate our
approach, we applied our model to an actual case of document processing, which
is a process of competitive bids for government projects in Japan. We used 100
documents for training and testing and confirmed that the model enables to
extract fine-grained named entities with a detailed level of information
preciseness specialized in the targeted business process, such as a department
name of application receivers.
