A hierarchical neural-network-based document representation approach for text classification
type: Item
instance of: scholarly article
title: A hierarchical neural-network-based document representation approach for text classification
zbMATH Open document ID: 1427.68281
DOI: 10.1155/2018/7987691
author: Jianming Zheng
author: Yupu Guo
author: Chong Feng
author: Honghui Chen
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Document representation is widely used in practical application, for example, sentiment classification, text retrieval, and text classification. Previous work is mainly based on the statistics and the neural networks, which suffer from data sparsity and model interpretability, respectively. In this paper, we propose a general framework for document representation with a hierarchical architecture. In particular, we incorporate the hierarchical architecture into three traditional neural-network models for document representation, resulting in three hierarchical neural representation models for document classification, that is, TextHFT, TextHRNN, and TextHCNN. Our comprehensive experimental results on two public datasets, that is, Yelp 2016 and Amazon Reviews (Electronics), show that our proposals with hierarchical architecture outperform the corresponding neural-network models for document classification, resulting in a significant improvement ranging from 4.65\% to 35.08\% in terms of accuracy with a comparable (or substantially less) expense of time consumption. In addition, we find that the long documents benefit more from the hierarchical architecture than the short ones as the improvement in terms of accuracy on long documents is greater than that on short documents.
Mathematics Subject Classification ID: 68T10
zbMATH DE Number: 7019527
describes a project that uses: word2vec
describes a project that uses: StanfordCoreNLP
describes a project that uses: GloVe
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/7987691
OpenAlex ID: W2795160845
cites work: 10.1162/153244303322533223
cites work: Q5396693
cites work: Q2934059
Wikidata QID: Q130047996
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description: scientific article; zbMATH DE number 7019527