Named entity recognition (NER) is a task of detecting named entities in documents and categorizing them to predefined classes, such as person, location, and organization.
The third experiment extends the same notion to relation annotation, resulting in a graph of entities and their relations per document, which gives rise to a more formalized notion of medical knowledge representation and personal knowledge management.
A covered entity should gather and document information on where ePHI is stored, maintained and transmitted.
Considering that we have two entities, and a large number of documents (over 50,000), it would be a challenge to determine the exact recall value for the two of them.
It also helps the researchers to find information which was once difficult to find by allowing one to tag documents with molecular entities and integrating with image recognition software to find information from pdf documents.
Linking chemical entities to the results obtained by biological/biomedical text mining systems requires first the automatic recognition and indexing of chemical entities in documents.
The authors conducted a survey of biological database curators and identified a 'canonical' workflow for biocuration, including the steps of (i) document selection, (ii) indexing of documents with biologically relevant entities and (iii) detailed curation of specific relations.
In future work, we will check for the presence of repeated entities between documents that could bias the NER evaluation between iterations and assess inter-annotator agreement between the five FlyBase curators to allow performance benchmarking.
This type of machine learning method together with logistic regression was also tested by team 90 (48), trying out many features, like type and text of named entities, words proximity to the entities and information on where in a document these entities where mentioned.
Collection of documents annotated with semantic entities and relationships are crucial resources to support development and evaluation of text mining solutions for the biomedical domain.
