Conference proceeding
Hierarchical-Document-Structure-Aware Attention with Adaptive Cost Sensitive Learning for Biomedical Document Classification
2019 IEEE International Conference on Big Data (Big Data), pp.1096-1102
01 Jan 2019
Abstract
Biomedical document classification is a fundamental task in biomedical field. Existing methods do not make full use of the hierarchically semantic structures in biomedical documents which can be utilized to improve the performance of biomedical document classification. In this paper, according to the hierarchical structures in given biomedical documents, we propose two models for biomedical document classification, which are based on the semantically hierarchical attention mechanism. Specifically, we utilize a hierarchical attention mechanism to model biomedical documents, taking into account simultaneously multiple-level semantic relationships in documents. In addition, an adaptive cost sensitive learning method is proposed to address the data imbalance issue. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed methods.
Metrics
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Details
- Title
- Hierarchical-Document-Structure-Aware Attention with Adaptive Cost Sensitive Learning for Biomedical Document Classification
- Creators
- Dandan Fang - Cent China Normal Univ, Sch Comp, Wuhan, Peoples R ChinaJinyong Zhang - Cent China Normal Univ, Sch Comp, Wuhan, Peoples R ChinaWeizhong Zhao - Cent China Normal Univ, Sch Comp, Wuhan, Peoples R ChinaXiaowei Xu - Univ Arkansas, Dept Informat Sci, Little Rock, AR 72204 USAXingpeng Jiang - Cent China Normal Univ, Sch Comp, Wuhan, Peoples R ChinaXiaohua Hu - Drexel UniversityTingting He - Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China
- Contributors
- C Baru (Editor)J Huan (Editor)L Khan (Editor)X H Hu (Editor)R Ak (Editor)Y Tian (Editor)R Barga (Editor)C Zaniolo (Editor)K Lee (Editor)Y F Ye (Editor)
- Publication Details
- 2019 IEEE International Conference on Big Data (Big Data), pp.1096-1102
- Conference
- 2019 IEEE International Conference on Big Data (Big Data)
- Series
- IEEE International Conference on Big Data
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- CCNU18JCXK05 / Key Research Program of Central China Normal University 2017YFC0909502 / National Key Research and Development Program of China 2019010701011392 / Wuhan Science and Technology Program kx201905 / Guangxi Key Laboratory of Trusted Software 61532008; 61872157 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) CCNU19TD004 / Fundamental Research Funds for the Central Universities
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Identifiers
- 991019170116504721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods