  Background: Finding biomedical named entities is one of the most essential
tasks in biomedical text mining. Recently, deep learning-based approaches have
been applied to biomedical named entity recognition (BioNER) and showed
promising results. However, as deep learning approaches need an abundant amount
of training data, a lack of data can hinder performance. BioNER datasets are
scarce resources and each dataset covers only a small subset of entity types.
Furthermore, many bio entities are polysemous, which is one of the major
obstacles in named entity recognition. Results: To address the lack of data and
the entity type misclassification problem, we propose CollaboNet which utilizes
a combination of multiple NER models. In CollaboNet, models trained on a
different dataset are connected to each other so that a target model obtains
information from other collaborator models to reduce false positives. Every
model is an expert on their target entity type and takes turns serving as a
target and a collaborator model during training time. The experimental results
show that CollaboNet can be used to greatly reduce the number of false
positives and misclassified entities including polysemous words. CollaboNet
achieved state-of-the-art performance in terms of precision, recall and F1
score. Conclusions: We demonstrated the benefits of combining multiple models
for BioNER. Our model has successfully reduced the number of misclassified
entities and improved the performance by leveraging multiple datasets annotated
for different entity types. Given the state-of-the-art performance of our
model, we believe that CollaboNet can improve the accuracy of downstream
biomedical text mining applications such as bio-entity relation extraction.

Computation and Language;Machine Learning