For the average biologist, hands-on literature mining currently means a keyword search in
PubMed. However, methods for extracting biomedical facts from the scientific literature have …

Scientific progress is increasingly based on knowledge and information. Knowledge is now
recognized as the driver of productivity and economic growth, leading to a new focus on the …

I examine what would be necessary to move part-of-speech tagging performance from its
current level of about 97.3% token accuracy (56% sentence accuracy) to close to 100 …

Abstract Information extraction is the task of finding structured information from unstructured
or semi-structured text. It is an important task in text mining and has been extensively studied …

Many named entities contain other named entities inside them. Despite this fact, the field of
named entity recognition has almost entirely ignored nested named entity recognition, but …

Background The goal of the first BioCreAtIvE challenge (Critical Assessment of Information
Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of …

The recognition of biomedical concepts in natural text (named entity recognition, NER) is a
key technology for automatic or semi-automatic analysis of textual resources. Precise NER …

Although recent named entity (NE) annotation efforts involve the markup of nested entities,
there has been limited focus on recognising such nested structures. This paper introduces …

Background The biological research literature is a major repository of knowledge. As the
amount of literature increases, it will get harder to find the information of interest on a …

We describe a machine learning system for the recognition of names in biomedical texts.
The system makes extensive use of local and syntactic features within the text, as well as …