Linked Document Embedding for Classification

@inproceedings{Wang2016LinkedDE,
  title={Linked Document Embedding for Classification},
  author={Suhang Wang and Jiliang Tang and Charu C. Aggarwal and Huan Liu},
  booktitle={CIKM},
  year={2016}
}
Word and document embedding algorithms such as Skip-gram and Paragraph Vector have been proven to help various text analysis tasks such as document classification, document clustering and information retrieval. The vast majority of these algorithms are designed to work with independent and identically distributed documents. However, in many real-world applications, documents are inherently linked. For example, web documents such as blogs and online news often have hyperlinks to other web… CONTINUE READING

Similar Papers

Citations

Publications citing this paper.
SHOWING 1-10 OF 33 CITATIONS

Network Representation Learning: A Survey

VIEW 7 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

DataSynapse: A Social Data Curation Foundry

  • Distributed and Parallel Databases
  • 2018
VIEW 1 EXCERPT
CITES BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 10 REFERENCES