Hierarchical-Document-Structure-Aware Attention with Adaptive Cost Sensitive Learning for Biomedical Document Classification

Fang, Dandan; Zhang, Jinyong; Zhao, Weizhong; Xu, Xiaowei; Jiang, Xingpeng; Hu, Xiaohua; He, Tingting

doi:10.1109/bigdata47090.2019.9006110

Cited by 1 publication

(1 citation statement)

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“…Since some abstracts do not have all six finer structures, the missing finer structures are padded by vectors with all zero as the input for Model_finer. We also compare the results of Model_base and Model_finer with our previous work 45 (denoted by "Model_base without GNN" and "Model_finer without GNN" in the following analysis) in which the GNN is not introduced for sentence modeling in each model. of dependency tree based GNN, which is to make full use of the semantic relationship between words to model the sentence representation.…”

Section: Evaluation Of Hierarchical Attention and Gnnmentioning

confidence: 99%

An effective framework for semistructured document classification via hierarchical attention model

Zhao

Fang

Zhang

et al. 2021

Int J of Intelligent Sys

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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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Section: Evaluation Of Hierarchical Attention and Gnnmentioning

confidence: 99%