Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Background Extracting biomedical entities and their relations from text has important
applications on biomedical research. Previous work primarily utilized feature-based pipeline …

Evidence-based dietary information represented as unstructured text is a crucial information
that needs to be accessed in order to help dietitians follow the new knowledge arrives daily …

Motivation Recognition of biomedical named entities in the textual literature is a highly
challenging research topic with great interest, playing as the prerequisite for extracting huge …

Past work in relation extraction mostly focuses on binary relation between entity pairs within
single sentence. Recently, the NLP community has gained interest in relation extraction in …

Motivation Understanding biological processes relies heavily on curated knowledge of
physical interactions between proteins. Yet, a notable gap remains between the information …

In the field of biomedical text mining, the ability to extract relations from the literature is
crucial for advancing both theoretical research and practical applications. There is a notable …

Medical relation extraction discovers relations between entity mentions in text, such as
research articles. For this task, dependency syntax has been recognized as a crucial source …

Event processing is an active area of research in the Natural Language Processing
community, but resources and automatic systems developed so far have mainly addressed …

Background Biomedical event extraction is a crucial task in biomedical text mining. As the
primary forum for international evaluation of different biomedical event extraction …