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- W4366308416 abstract "With the rapid development of communication networks, the number of proposals in professional fields has increased explosively. In the era of big data, topic classification and retrieval of proposals has become an important demand for researchers. However, the automatic topic classification of proposals in the professional field has the characteristics of high semantic similarity, that is, fine-grained classification. It is difficult for text classification models in general fields to achieve high-precision classification effects on proposal datasets in professional fields. At the same time, the problem of class imbalance reduces the effective training of the model for minority classes, making the widespread application of the model difficult. In order to solve the proposal classification in professional fields, this paper proposes a keyphrase-enhanced graph convolutional network (KPE-GCN), which uses keyphrase-based data augmentation to effectively alleviate the problem of class imbalance. Meanwhile, we build a two-level heterogeneous graph that combines both word-level and keyphrase-level information, which can add domain features to the proposal structure. It can effectively distinguish the differences between fine-grained categories, and improve the accuracy of fine-grained classification of proposal. We perform extensive experiments on the proposal dataset, and KPE-GCN model can exhibit higher classification performance, raising the F1 value to over 99% for the first time. Our KPE-GCN model provides a unified domain feature extraction scheme, which can be widely used in various professional fields." @default.
- W4366308416 created "2023-04-20" @default.
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- W4366308416 date "2022-10-01" @default.
- W4366308416 modified "2023-10-14" @default.
- W4366308416 title "KPE-GCN: A Keyphrase-Enhanced Graph Convolutional Network for Imbalanced Text Classification" @default.
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- W4366308416 doi "https://doi.org/10.1109/ictai56018.2022.00050" @default.
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