hyperdoc2vec: Distributed Representations of Hypertext Documents

Jialong Han, Yan Song, Wayne Xin Zhao, Shuming Shi, Haisong Zhang


Abstract
Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life. Although being effective on plain documents, conventional text embedding methods suffer from information loss if directly adapted to hyper-documents. In this paper, we propose a general embedding approach for hyper-documents, namely, hyperdoc2vec, along with four criteria characterizing necessary information that hyper-document embedding models should preserve. Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks, i.e., paper classification and citation recommendation, in the academic paper domain. Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t. the four criteria.
Anthology ID:
P18-1222
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2384–2394
Language:
URL:
https://aclanthology.org/P18-1222
DOI:
10.18653/v1/P18-1222
Bibkey:
Cite (ACL):
Jialong Han, Yan Song, Wayne Xin Zhao, Shuming Shi, and Haisong Zhang. 2018. hyperdoc2vec: Distributed Representations of Hypertext Documents. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2384–2394, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
hyperdoc2vec: Distributed Representations of Hypertext Documents (Han et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1222.pdf
Poster:
 P18-1222.Poster.pdf