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Journal article
An effective framework for semistructured document classification via hierarchical attention model
International journal of intelligent systems, v 36(9), pp 5161-5183
01 Sep 2021
Abstract
Recent years have witnessed the rapidly growing of the amount of semistructured documents in real-world applications. Due to the huge size of the real-world data, how to manage semistructured documents effectively is a big challenge for researchers. As a fundamental task in natural language processing field, document classification is a feasible way to handle the large-scale semistructured documents. However, existing methods fail to explicitly take advantage of the hierarchical semantics in semistructured documents. It's known that the contained semantics is beneficial for understanding the semistructured documents. Considering the hierarchical structure of a given semistructured document, we propose a semistructured document classification framework which explicitly utilizes the semantic hierarchical attention mechanism. More specifically, the hierarchical attention mechanism and graph neural network are employed to model semistructured documents, by which the multilevel semantic relationships and grammatical information are considered. Moreover, we propose an adaptive class cost learning method to treat the issue of data imbalance. Comprehensive experiments are conducted on two real-world data sets, and the results demonstrate that our framework performs better than selected baselines for semistructured document classification.
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Details
- Title
- An effective framework for semistructured document classification via hierarchical attention model
- Creators
- Weizhong Zhao - Central China Normal UniversityDandan Fang - Central China Normal UniversityJinyong Zhang - Central China Normal UniversityYao Zhao - Central China Normal UniversityXiaowei Xu - University of Arkansas at Little RockXingpeng Jiang - Central China Normal UniversityXiaohua Hu - Drexel UniversityTingting He - Central China Normal University
- Publication Details
- International journal of intelligent systems, v 36(9), pp 5161-5183
- Publisher
- Wiley
- Number of pages
- 23
- Grant note
- 61532008; 61872157; 61932008 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) 2019010701011392 / Wuhan Science and Technology Program MIMS19-02 / Research Fund of Guangxi Key Lab of Multi-source Information Mining Security kx201905 / Guangxi Key Laboratory of Trusted Software 2020BAB017 / Key Research and Development Program of Hubei Province CCNU19TD004 / Fundamental Research Funds for the Central Universities
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000656113400001
- Scopus ID
- 2-s2.0-85106981121
- Other Identifier
- 991019167684804721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence