Named entity recognition (NER) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. We use the JavaNLP repository(http://nlp.stanford.edu/javanlp/ ) for its implementation of a Conditional Random Field...

The BioCreative IV CHEMDNER Task provides participants with the opportunity to compare their methods for chemical named entity recognition (NER) and indexing in a controlled environment. We contributed to this task with our previous conditional random field based system [1] extended by a number of novel general and domain-specific features. For the latter, we used features derived from two exis...

Chemical entities are ubiquitous through the biomedical literature and the development of text-mining systems that can efficiently identify those entities are required. Due to the lack of available corpora and data resources, the community has focused its efforts in the development of gene and protein named entity recognition systems, but with the release of ChEBI and the availability of an ann...

This paper relates to the two offline BioCreative V.5 Becalm tasks. The first challenge is CEMP, the recognition of chemical named entity mentions. The second challenge is GPRO, the recognition of gene and protein related objects in running text. We focus on training and optimizing state-of-the-art solutions for named entity tagging for CEMP and GPRO. Finally, we present CRFVoter, a two staged ...

We describe the semantic enrichment of journal articles with chemical structures and biomedical ontology terms using Oscar, a program for chemical named entity recognition (NER). We describe how Oscar works and how it can been adapted for general NER. We discuss its implementation in a real publishing workflow and possible applications for enriched articles.

Chemical named entity recognition has traditionally been dominated by CRF (Conditional Random Fields)-based approaches but given the success of WKH DUWLILFLDO QHXUDO QHWZRUN WHFKQLTXHV NQRZQ DV 3GHHS OHDUQLQJ ́ Ze decided to examine them as an alternative to CRFs. We present here three systems. The first system translates the traditional CRF-based idioms into a deep learning framework, using ric...

MOTIVATION The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be supported by computational tools. RESULTS We...

As biomedical literature continues to grow at an explosive rate, researchers are unable to process the vast amounts of information generated by one another. In order to account for this, text mining and information extraction systems have been developed in order to help researchers find information that is relevant to their respective research. However, text mining systems have also been develo...

Chemical compounds and drugs are an important class of entities in biomedical research with great potential in a wide range of applications, including clinical medicine. Locating chemical named entities in the literature is a useful step in chemical text mining pipelines for identifying the chemical mentions, their properties, and their relationships as discussed in the literature. We introduce...