Research Sharing-Oriented Functional Neuroimaging Named Entity Recognition - Details

Author：

Sheng, Ying (Sheng, Ying.) | Lin, Shaofu (Lin, Shaofu.) | Gao, Jiangfan (Gao, Jiangfan.) | He, Xiaobo (He, Xiaobo.) | Chen, Jianhui (Chen, Jianhui.)

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Abstract：

Named　entity　recognition　is　a　basic　and　core　task　of　information　extraction　on　functional　neuroimaging　literatures.　However,　existing　researches　only　focus　on　cognitive　states　and　brain　regions,　and　are　far　from　effective　research　sharing.　This　paper　proposes　a　research　sharing-oriented　approach　for　functional　neuroimaging　named　entity　recognition.　The　nine　most　representative　entity　categories　were　defined　by　analyzing　the　characteristics　of　task-based　functional　neuroimaging　researches,　and　a　multi-category　named　entity　recognition　method　was　designed　based　on　BiLSTM-CNN.　An　experiment　was　performed　on　literatures　obtained　from　the　journal　PLoS　One.　The　experimental　results　show　that　the　precision　and　recall　rates　of　the　proposed　method　can　reach　94.50%　and　95.56%,　and　are　obviously　superior　to　existing　methods　of　functional　neuroimaing　named　entity　recognition.　©　2019　IEEE.

Keyword：

Functional neuroimaging Bioinformatics Brain

Author Community：

[ 1 ] [Sheng, Ying]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 2 ] [Lin, Shaofu]Beijing Institute of Smart City, Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 3 ] [Gao, Jiangfan]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 4 ] [He, Xiaobo]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 5 ] [Chen, Jianhui]Beijing Key Laboratory of MRI and Brain Informatics, Beijing University of Technology, Beijing, China