Representing words as numerical vectors based on the contexts in which they appear has
become the de facto method of analyzing text with machine learning. In this paper, we …

The widespread use of electronic health records (EHR) systems in health care provides a
large amount of real-world data, leading to new areas for clinical research. Natural language …

Open domain question answering (OpenQA) tasks have been recently attracting more and
more attention from the natural language processing (NLP) community. In this work, we …

We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected
from PubMed abstracts. The task of PubMedQA is to answer research questions with …

State-of-the-art models in NLP are now predominantly based on deep neural networks that
are opaque in terms of how they come to make predictions. This limitation has increased …

Motivation Biomedical text mining is becoming increasingly important as the number of
biomedical documents rapidly grows. With the progress in natural language processing …

State-of-the-art models for joint entity recognition and relation extraction strongly rely on
external natural language processing (NLP) tools such as POS (part-of-speech) taggers and …

Motivation Text mining has become an important tool for biomedical research. The most
fundamental text-mining task is the recognition of biomedical named entities (NER), such as …

Abstract Clinical Named Entity Recognition (CNER) is a critical task which aims to identify
and classify clinical terms in electronic medical records. In recent years, deep neural …

Motivation State-of-the-art biomedical named entity recognition (BioNER) systems often
require handcrafted features specific to each entity type, such as genes, chemicals and …