Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification

Wang, Yaqing; Wang, Song; Yao, Quanming; Dou, Dejing

doi:10.48550/arxiv.2111.00180

Cited by 1 publication

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HGAT [36]: This model is a heterogeneous GNN-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph. SHINE [37]: This model first models the short text as a hierarchical heterogeneous graph consisting of word-level component graphs which introduce more semantic and syntactic information. Then, the model dynamically learns a short document graph to facilitate effective label propagation among similar short texts.…”

Section: B Baseline Methodsmentioning

confidence: 99%

“…Yang et al [36] proposed a novel heterogeneous Graph Neural Network (GNN)-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph. Wang et al [37] proposed SHINE, which used Graph Neural Network (GNN) for short text classification. Wang et al [38] proposed a short text classification method based on semantic extension and CNN.…”

Section: B Short Text Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Word-Level and Pinyin-Level Based Chinese Short Text Classification

Sun

Huo

2022

IEEE Access

View full text Add to dashboard Cite

Short text classification is an important branch of Natural Language Processing. Although CNN and RNN have achieved satisfactory results in the text classification tasks, they are difficult to apply to the Chinese short text classification because of the data sparsity and the homophonic typos problems of them. To solve the above problems, word-level and Pinyin-level based Chinese short text classification model is constructed. Since homophones have the same Pinyin, the addition of Pinyin-level features can solve the homophonic typos problem. In addition, due to the introduction of more features, the data sparsity problem of short text can be solved. In order to fully extract the deep hidden features of the short text, a deep learning model based on BiLSTM, Attention and CNN is constructed, and the residual network is used to solve the gradient disappearance problem with the increase of network layers. Additionally, considering that the complex deep learning network structure will increase the text classification time, the Text Center is constructed. When there is a new text input, the text classification task can be quickly realized by calculating the Manhattan distance between the embedding vector of it and the vectors stored in the Text Center. The Accuracy, Precision, Recall and F1 of the proposed model on the simplifyweibo_4_moods dataset are 0.9713, 0.9627, 0.9765 and 0.9696 respectively, and those on the online_shopping_10_cats dataset are 0.9533, 0.9416, 0.9608 and 0.9511 respectively, which are better than that of the baseline method. In addition, the classification time of the proposed model on simplifyweibo_4_moods and online_shopping_10_cats is 0.0042 and 0.0033 respectively, which is far lower than that of the baseline method.

show abstract

Section: B Baseline Methodsmentioning

confidence: 99%