Efficient feedback-based feature learning for blog distillation as a terabyte challenge

Dehong Gao; Wenjie Li; Renxian Zhang

doi:10.1142/9789813223615_0015

Efficient feedback-based feature learning for blog distillation as a terabyte challenge

Dehong Gao
, Wenjie Li
, Renxian Zhang

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research › peer-review

1 Citation (Scopus)

Abstract

The chapter is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features, including the unigrams as well as the patterns of unigram associations. Meanwhile facing the terabyte blog dataset, some flexible processing is adopted in our approach. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.

Original language	English
Title of host publication	Social Media Content Analysis
Subtitle of host publication	Natural Language Processing and Beyond
Publisher	World Scientific Publishing Co. Pte. Ltd.
Pages	215-224
Number of pages	10
ISBN (Electronic)	9789813223615
ISBN (Print)	9789813223608
DOIs	https://doi.org/10.1142/9789813223615_0015
Publication status	Published - 1 Jan 2017

ASJC Scopus subject areas

General Computer Science

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10.1142/9789813223615_0015

Cite this

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Efficient feedback-based feature learning for blog distillation as a terabyte challenge. / Gao, Dehong; Li, Wenjie; Zhang, Renxian.
Social Media Content Analysis: Natural Language Processing and Beyond. World Scientific Publishing Co. Pte. Ltd., 2017. p. 215-224.

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research › peer-review

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Efficient feedback-based feature learning for blog distillation as a terabyte challenge

Abstract

ASJC Scopus subject areas

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