Conditional Random Fields for Biomedical Named Entity Recognition Revisited
Named Entity Recognition (NER) is a key task which automatically extracts Named Entities (NE) from the text. Names of persons, places, date and time are examples of NEs. We are applying Conditional Random Fields (CRFs) for NER in biomedical domain. Examples of NEs in biomedical texts are gene, proteins. We used a minimal set of features to train CRF algorithm and obtained a good results for biomedical texts.
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
Posted 23 Jun, 2020
Conditional Random Fields for Biomedical Named Entity Recognition Revisited
Posted 23 Jun, 2020
Named Entity Recognition (NER) is a key task which automatically extracts Named Entities (NE) from the text. Names of persons, places, date and time are examples of NEs. We are applying Conditional Random Fields (CRFs) for NER in biomedical domain. Examples of NEs in biomedical texts are gene, proteins. We used a minimal set of features to train CRF algorithm and obtained a good results for biomedical texts.
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.