Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/252122
Title: Biomedical Named Entity Recognition
Researcher: Hamada Ali Mohamed Ali Nayel
Guide(s): H. L. Shashirekha
Keywords: Engineering and Technology,Computer Science,Operations Research and Management Science
University: Mangalore University
Completed Date: 
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.
Pagination: 134
URI: http://hdl.handle.net/10603/252122
Appears in Departments:Department of Computer Science

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01_title page.pdfAttached File15.68 kBAdobe PDFView/Open
02_certificate.pdf317.73 kBAdobe PDFView/Open
03_declaration.pdf317.75 kBAdobe PDFView/Open
04_acknowledgement.pdf61.88 kBAdobe PDFView/Open
05_abstract.pdf44.82 kBAdobe PDFView/Open
06_contents.pdf55.3 kBAdobe PDFView/Open
07_list of tables and figures.pdf64.93 kBAdobe PDFView/Open
08_chapter 1.pdf80.49 kBAdobe PDFView/Open
09_chapter 2.pdf756.42 kBAdobe PDFView/Open
10_chapter 3.pdf1.16 MBAdobe PDFView/Open
11_chapter 4.pdf528.53 kBAdobe PDFView/Open
12_chapter 5.pdf1.31 MBAdobe PDFView/Open
13_chapter 6.pdf368.58 kBAdobe PDFView/Open
14_bibliography.pdf118.5 kBAdobe PDFView/Open
15_appendix.pdf93.47 kBAdobe PDFView/Open


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