2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Ad 2014
DOI: 10.1109/scis-isis.2014.7044640
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Abstract: Biomedical named entity recognition (BNER) is one of the most essential and initial tasks (discovering relations between biomedical entities, identifying molecular pathways, etc.) of biomedical information retrieval. Although named entity recognition performed well in ordinary text, it still remains challenging in molecular biology domain because of the complex nature of biomedical nomenclature, different kinds of spelling forms and many more reasons. Even though biomedical entities in biological text are foun… Show more

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Cited by 4 publications
(4 citation statements)
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“…As shown in Figure 4, the highest classification performance was obtained with the proposed combination of PS can be considered as a dynamic feature because it is based on the number of MEDLINE abstracts cited in the PubMed database, which are gradually increasing. In Sumathipala et al (2014) [19] we introduced a unithood measure called Proteinhood which quantified the dependency between sub-terms of biomedical term candidates by measuring the probabilistic strength of forming a PNE. Proteinhood values were estimated using the protein sub-terms in the training data set.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…As shown in Figure 4, the highest classification performance was obtained with the proposed combination of PS can be considered as a dynamic feature because it is based on the number of MEDLINE abstracts cited in the PubMed database, which are gradually increasing. In Sumathipala et al (2014) [19] we introduced a unithood measure called Proteinhood which quantified the dependency between sub-terms of biomedical term candidates by measuring the probabilistic strength of forming a PNE. Proteinhood values were estimated using the protein sub-terms in the training data set.…”
Section: Resultsmentioning
confidence: 99%
“…In the study of [19], we conducted experiments using several ML techniques, and RF performed the best. RF is a powerful classification algorithm in the group of ensemble learning and obtains growing attention on these days.…”
Section: Experiments and Evaluationmentioning
confidence: 99%
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“…However, the problem with those approaches has used a dictionary-based approach to identify named entities in the medical domain which lacks in accuracy after several years because of the arrival of new biomedical entities to the field. There are machine learning based and web-based biomedical entity recognition systems proposed [3][4][5][6]. However, most of these approaches only consider the specific type of biomedical entities such as proteins.…”
Section: Related Workmentioning
confidence: 99%