When the How Outweighs the What: the Pivotal Importance of Context A growing body of knowledge about biological mechanisms and interaction of biological components is contained in the peerreviewed scientific literature. In order to leverage this knowledge towards the development of predictive models, one must first extract these relationships from the text. However, the context in which the interaction was reported is critical in ensuring that it is used in a manner consistent with the modelu0027s intended application. Here we assess the applicability of two generic automated methods for leveraging a broader contextual structure in the more specific domain of a biological experiment using only the paperu0027s title and abstract. In an example use case, a Support Vector Machine (SVM) and two variants of the broadlyused Bidirectional Encoder Representations from Transformers (BERT) neural network model, serve to distinguish mouse from human subject experiments in a corpus of over 12,000 papers documenting mechanistic interactions in a regulatory model of of mucosal immune signaling. The BERT and domainspecific BioBERT yielded essentially equivalent classification accuracy with both neural network models performing only marginally better than the SVM. Words occurring frequently in abstracts were largely nonspecific, whereas words unique to each class were used in 4% or less of the abstracts. These highspecificity words were used in very similar contexts that separated mouse and human study abstracts on the basis of study design and experimental procedure rather than species or basic biological markers.

Hierarchical QuestionAware Context Learning with Augmented Data for Biomedical Question Answering This paper is concerned with the task of biomedical Question Answering (QA) which refers to extracting an answer to the given question from a biomedical context. Current works have made progress on this task, but they are still severely restricted by the insufficient training data due to the domainspecific nature, which motivates us to further explore a powerful way to solve this problem. We propose a Hierarchical QuestionAware Context Learning (HQACL) model for the biomedical QA task constituted by multilevel attention. The interaction between the question and the context can be captured layer by layer, with multigrained embeddings to strengthen the ability of the language representation. A special training method called DA, including two parts namely domain adaptation and data augmentation, is also introduced to enhance the model performance. Domain adaptation can be defined as pretraining on a largescale opendomain dataset and finetuning on the small training set of the target domain. As for the data augmentation, the Roundtrip translation method is adopted to create new data with various expressions, which almost doubles the training set. The public biomedical dataset collected from PubMed provided by BioASQ is used to evaluate our model. The results show that our approach is superior to the best recent solution and achieves a new state of the art.