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Text classification on heterogeneous information network via enhanced GCN and knowledge

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Abstract

Graph convolutional networks-based text classification methods have shown impressive success in further improving the classification results by considering the structural relationship between words and texts. However, existing GCN-based text classification methods tend to ignore the semantic representation of the node and the global structural information among nodes. Besides, only the word granularity information within the text, i.e., endogenous source, is used to represent the text. Furthermore, the existing graph convolutional network approaches are faced with major challenges to handle large and dense graphs, i.e., neighbor explosion and noisy inputs. To address these shortcomings, this paper proposes an inductive learning-based text classification method that utilizes representation learning on heterogeneous information networks and exogenous knowledge. Firstly, a weighted heterogeneous information network for text (HINT) is constructed by introducing exogenous knowledge, in which the node types cover text, entities and words. The unstructured text is represented as a structured heterogeneous information network, which expands the granularity of text features and makes full use of the exogenous structural information and explicit semantic information to enhance the interpretability of text information. Besides, we also enhanced the graph neural network against the challenges of neighbor explosion and noisy inputs derived from HINT using two strategies: graph sampling and Dropedge, for semi-supervised learning with improved classification performance. The effectiveness of our model is demonstrated by examining four publicly available text classification datasets. Based on experimental results, our approach achieves state-of-the-art performance on the text classification datasets.

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Data Availability

The R8 and R52 data that support the findings of this study are available in/from the Reuters-21,578 corpus, http://www.daviddlewis.co. The Ohsumed data that support the findings of this study are available in/from the Text Categorization corpora, http://disi.unitn.it/moschitti/corpora.htm. The TREC data that support the findings of this study are available in/from the TERC corpus, https://trec.nist.gov/data.html. The 20NG data that support the findings of this study are available in/from the 20Newsgroups, https://trec.nist.gov/data.html. The MR data that support the findings of this study are available in/from the movie review corpus, https://www.cs.cornell.edu/people/pabo/movie-review-data/.

Notes

  1. http://www.daviddlewis.com/.

  2. http://www.daviddlewis.com/.

  3. https://trec.nist.gov/data.html.

  4. http://qwone.com/~jason/20Newsgroups/.

  5. http://www.cs.cornell.edu/people/pabo/movie-review-data/.

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Funding

This research was funded partially by Innovation Program of Chinese Academy of Agricultural Sciences (Grant No. CAAS-ASTIP-2021-AII-06), Central Public-interest Scientific Institution Basal Research Fund (Grant No. key laboratory open subject of Agricultural Information Research Institute 22), Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China, Digital agriculture technology system Beijing innovation team (Grant No. BAIC10-2022-E10).

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Authors and Affiliations

  1. School of Labor Economics, Capital University of Economies and Business, Beijing, China

    Hui Li

  2. Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural, Beijing, China

    Juan Liu & Yunpeng Cui

  3. Agriculture Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China

    Juan Liu & Yunpeng Cui

  4. Data61, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Australia

    Shuo Wang

  5. Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, China

    Yan Yan

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  1. Hui Li
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Correspondence to Yan Yan, Shuo Wang or Juan Liu.

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Li, H., Yan, Y., Wang, S. et al. Text classification on heterogeneous information network via enhanced GCN and knowledge. Neural Comput & Applic 35, 14911–14927 (2023). https://doi.org/10.1007/s00521-023-08494-0

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