Improving Open Directory Project-Based Text Classification with Hierarchical Category Embedding

Ji Min Lee, Kang Min Kim, Yeachan Kim, Sang-Geun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Many works have used knowledge bases that contain taxonomy of hierarchically structured categories for large-scale text classification. These works have utilized hierarchical taxonomies based on the explicit representation model. They demonstrated that the explicit representation model provides a stable performance for large-scale text classification. However, this performance is limited to the knowledge base. In this paper, we integrate the implicit representation model, which has the ability to use external knowledge indirectly, with previous large-scale text classification. To this end, we first propose Hierarchical Category embedding (HC embedding) to generate distributed representations of hierarchical categories based on the implicit representation model. Second, we develop a new semantic similarity method to integrate HC embedding with the large-scale text classification. To demonstrate efficacy, we apply the proposed methodology to Open Directory Project (ODP)-based text classification, which has a hierarchical taxonomy. The evaluation results demonstrate that the proposed method outperforms the current state-of-the-art method by 7.4 %, 7.0 %, and 18 % in terms of micro-averaging F1-score, macro-averaging F1-score, and precision at k, respectively.

Original languageEnglish
Title of host publicationProceedings of 2018 IEEE 17th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018
EditorsNewton Howard, Sam Kwong, Yingxu Wang, Jerome Feldman, Bernard Widrow, Phillip Sheu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages246-253
Number of pages8
ISBN (Electronic)9781538633601
DOIs
Publication statusPublished - 2018 Oct 4
Event17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018 - Berkeley, United States
Duration: 2018 Jul 162018 Jul 18

Publication series

NameProceedings of 2018 IEEE 17th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018

Other

Other17th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2018
Country/TerritoryUnited States
CityBerkeley
Period18/7/1618/7/18

Bibliographical note

Funding Information:
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (number 2015R1A2A1A10052665). This research was also in part supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2018-2016-0-00464) supervised by the IITP(Institute for Information & communications Technology Promotion).

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Artificial neural networks
  • Embedding
  • Knowledge manipulations
  • Knowledge representation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Cognitive Neuroscience

Fingerprint

Dive into the research topics of 'Improving Open Directory Project-Based Text Classification with Hierarchical Category Embedding'. Together they form a unique fingerprint.

Cite this