Nested Biomedical Named Entity Recognition
Lobna Mady; Afify, Yasmine M.; Nagwa Badr;
Abstract
Named entity recognition has been regarded as an important task in natural language
processing. Extracting biomedical entities such as RNAs, DNAs, cell lines, proteins, and cell types has
been recognized as a challenging task. Most of the existing research focuses on the extraction of flat
named entities only and ignores the nested entities. Nested entities, on the other hand, are commonly
used in real world biomedical applications due to their ability to represent semantic meaning of the
named entity. This paper proposes an approach to improve the performance of nested biomedical
named entity recognition by using a combination of diverse types of features namely morphological,
orthographical, context, part of speech and word representation features while using Structured
Support Vector Machine as a machine learning technique. The results obtained from the proposed
approach were compared with those from popular benchmark approaches. The popular dataset
“Genia” is utilized to evaluate the proposed approach which achieved Recall, Precision and F1-
Measure of 84.033%, 85.946 %, and 84.113% respectively.
processing. Extracting biomedical entities such as RNAs, DNAs, cell lines, proteins, and cell types has
been recognized as a challenging task. Most of the existing research focuses on the extraction of flat
named entities only and ignores the nested entities. Nested entities, on the other hand, are commonly
used in real world biomedical applications due to their ability to represent semantic meaning of the
named entity. This paper proposes an approach to improve the performance of nested biomedical
named entity recognition by using a combination of diverse types of features namely morphological,
orthographical, context, part of speech and word representation features while using Structured
Support Vector Machine as a machine learning technique. The results obtained from the proposed
approach were compared with those from popular benchmark approaches. The popular dataset
“Genia” is utilized to evaluate the proposed approach which achieved Recall, Precision and F1-
Measure of 84.033%, 85.946 %, and 84.113% respectively.
Other data
Title | Nested Biomedical Named Entity Recognition | Authors | Lobna Mady; Afify, Yasmine M. ; Nagwa Badr | Keywords | Machine Learning, Nested Entities, Classification, Biomedical Named Entity Recognition | Issue Date | 2021 | Journal | International Journal of Intelligent Computing, and Information Sciences (IJICIS) | Volume | 22 | Issue | 1 | DOI | 10.21608/ijicis.2022.104170.1134 |
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