Biomedical Named Entity Recognition

Abstract

newline Named Entity Recognition (NER) is a crucial Natural Language Processing (NLP) task whichextractsNamedEntities(NE)fromthetext. Namesofpersons,places,dateandtime are examples of NEs in general domain texts, while names of genes, proteins and diseases are examples of NEs in biomedical domain termed as BioNE. NER in Biomedical domain (BioNER) is an important preprocessing task for many further tasks such as relation extraction between entities, knowledge discovery and hypothesis generation. The tremendousgrowthofpublicationsinbiomedicalresearchareamakesitvitaltoapplyBioNERas it is tough to extract NEs manually. Furthermore, BioNEs pose several challenges related to ambiguous names, synonyms, variations, multi-word NEs and nested NEs. newlineDifferent approaches have been used for BioNER, such as dictionary approaches, rulebasedapproaches,MachineLearning(ML)approachesandhybridapproaches. OflateML approaches specially Artiand#64257;cial Neural Network based models are popularly being used for BioNER. Annotating the dataset for training the models to recognize and classify NEs is a crucial task in BioNER. There are many methods used for annotating the datasets such as XML format, BioNEs offset and Segment Representation (SR). SR is an efand#64257;cient way of annotating BioNEs within a sentence in order to differentiate them from non-BioNEs. Different SR schemes such as IO, IOE2, IOB2, IOBE and IOBES are used to annotate the dataset to develop efand#64257;cient BioNER systems.