The number of scientific publications in the literature is steadily growing, containing our
knowledge in the biomedical, health, and clinical sciences. Since there is currently no …

Transformer-based pretrained language models (PLMs) have started a new era in modern
natural language processing (NLP). These models combine the power of transformers …

Distributed word representations have become an essential foundation for biomedical
natural language processing (BioNLP), text mining and information retrieval. Word …

Automated relation extraction (RE) from biomedical literature is critical for many downstream
text mining applications in both research and real-world settings. However, most existing …

With the continuous expansion of data availability in many large-scale, complex, and
networked systems, such as surveillance, security, Internet, and finance, it becomes critical …

Receiver Operator Characteristic (ROC) curves are commonly used to present results for
binary decision problems in machine learning. However, when dealing with highly skewed …

The automatic extraction of information from unstructured sources has opened up new
avenues for querying, organizing, and analyzing data by drawing upon the clean semantics …

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

Software fault prediction aims to identify fault-prone software modules by using some
underlying properties of the software project before the actual testing process begins. It …

We present a new kernel method for extracting semantic relations between entities in natural
language text, based on a generalization of subsequence kernels. This kernel uses three …