Enhancing Performance of Biomedical Named Entity Recognition
Lobna Mady; Afify, Yasmine M.; Nagwa Badr;
Abstract
Identifying biomedical entities such as genes,
proteins, cell types, and cell lines in the field of named entity
recognition has been acknowledged as a challenging task. In this
paper, the performance of biomedical named entity recognition
has been enhanced by using a filter-based feature selection
technique. Various feature types have been considered in the
selection process such as morphological, orthographical, part of
speech, context, and word embedding. The Chi-squared and
ReliefF feature selection techniques have been utilized to reduce
the feature vector, which is then taken as an input to structured
support vector machine to extract biomedical entities. An
innovative approach has been proposed and evaluated on the
popular dataset “Genia” using common evaluation metrics.
Results revealed that the whole performance of biomedical
named entity recognition increased when using the adopted
feature selection techniques
proteins, cell types, and cell lines in the field of named entity
recognition has been acknowledged as a challenging task. In this
paper, the performance of biomedical named entity recognition
has been enhanced by using a filter-based feature selection
technique. Various feature types have been considered in the
selection process such as morphological, orthographical, part of
speech, context, and word embedding. The Chi-squared and
ReliefF feature selection techniques have been utilized to reduce
the feature vector, which is then taken as an input to structured
support vector machine to extract biomedical entities. An
innovative approach has been proposed and evaluated on the
popular dataset “Genia” using common evaluation metrics.
Results revealed that the whole performance of biomedical
named entity recognition increased when using the adopted
feature selection techniques
Other data
Title | Enhancing Performance of Biomedical Named Entity Recognition | Authors | Lobna Mady; Afify, Yasmine M. ; Nagwa Badr | Keywords | Biomedical Named Entity Recognition, Machine Learning, Structured Support Vector Machine, Feature Selection | Issue Date | 2021 | Publisher | Tenth International Conference on Intelligent Computing and Information Systems (ICICIS) | Start page | 467 | End page | 472 | DOI | 10.1109/ICICIS52592.2021.9694128 |
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