Paper Info
Title
An Effective Framework For Semistructured Document Classification Via Hierarchical Attention Model
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.
Year
DOI
Venue
2021
10.1002/int.22508
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Keywords
DocType
Volume
adaptive class cost learning, attention model, document classification, graph neural network, semistructured data, semantic hierarchy
Journal
36
Issue
ISSN
Citations 
9
0884-8173
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Weizhong Zhao113.73
Dandan Fang200.34
Jinyong Zhang312.04
Yao Zhao4325.94
Xiaowei Xu5162.59
Xingpeng Jiang600.34
Xiaohua Hu721.44
Tingting He800.68