MultiCGCN: Multi-Label Text Classification using GCNs and Heterogeneous Graphs

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Allahgholi Milad; Rahmani Hossein; Soltanzadeh Parinaz; Naebzadeh Aylin

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF WEB RESEARCH Year:2024 | Volume:7 | Issue:4 Page(s): 29-37

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

MultiCGCN: Multi-Label Text Classification using GCNs and Heterogeneous Graphs

Author(s)

Allahgholi Milad | Rahmani Hossein | Soltanzadeh Parinaz | Naebzadeh Aylin | Issue Writer Certificate

Keywords

Text Classification

Graph Convolutional Neural Networks

Multi-label Text Classification

Abstract

Multi-label text classification is a critical challenge in natural language processing, where the goal is to assign multiple labels to a given document. Recent advances have primarily focused on deep learning approaches, yet many fail to adequately capture the intricate relationships between documents and labels. In this paper, we propose a novel method called MultiCGCN, in which we leverage Graph Convolutional Networks (GCNs) for multi-label text classification by modeling text as a heterogeneous graph. This unified graph incorporates document similarities, label relationships, and document-label associations, enabling the model to effectively capture both document and label dependencies. We transform the multi-label classification problem into a link prediction task, using Term Frequency–Inverse Document Frequency (TF-IDF) for document similarity and applying GCNs to predict label assignments. Our empirical evaluations demonstrate that MultiCGCN achieves a significant performance boost, improving F1 score by 10% over traditional baseline models. This approach opens new avenues for enhancing the accuracy of multi-label classification in various domains.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans