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International Conference on Database Systems for Advanced Applications

DASFAA 2017: Database Systems for Advanced Applications pp 50–67Cite as

Category-Level Transfer Learning from Knowledge Base to Microblog Stream for Accurate Event Detection

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10177))

Abstract

Many Web applications need the accurate event detection technique on microblog stream. But the accuracy of existing methods is still challenged by microblog’s short length and high noise. We develop a novel category-level transfer learning method TransDetector to deal with the task. TransDetector bases on two facts, that microblog is short but can be enriched by knowledge base semantically with transfer learning; and events can be detected more accurately on microblogs with richer semantics. The following contributions are made in TransDetector. (1) We propose a structure-guided category-level topics extraction method, which exploits the knowledge base’s hierarchical structure to extract categories’ highly correlated topics. (2) We develop a probabilistic model CTrans-LDA for category-level transfer learning, which utilizes the word co-occurrences and transfers the knowledge base’s category-level topics into microblogs. (3) Events are detected accurately on category-level word time series, due to richer semantics and less noise. (4) Experiment verifies the quality of category-level topics extracted from knowledge base, and the further study on the benchmark Edinburgh twitter corpus validates the effectiveness of our proposed transfer learning method for event detection. TransDetector achieves high accuracy, promoting the precision by 9% without sacrificing the recall rate.

This research is supported by the Natural Science Foundation of China (Grant No. 61572043), and the National Key Research and Development Program (Grant No. 2016YFB1000704).

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Portal:Current_events.

  2. 2.

    https://en.wikipedia.org/wiki/Category:Main_topic_classifications.

  3. 3.

    https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-categorylinks.sql.gz.

  4. 4.

    https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2.

  5. 5.

    http://demeter.inf.ed.ac.uk/cross/docs/fsd_corpus.tar.gz.

  6. 6.

    https://github.com/AKSW/Palmetto.

  7. 7.

    https://github.com/Falitokiniaina/EDCoW.

  8. 8.

    https://github.com/xiaohuiyan/BurstyBTM.

  9. 9.

    http://demeter.inf.ed.ac.uk/cross/docs/Newswire_Events.tar.gz.

  10. 10.

    https://en.wikipedia.org/wiki/Portal:Current_events.

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Acknowledgments

Thanks to Dr. Jian Tang and the anonymous reviewers for giving valuable suggestions on this work.

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Authors and Affiliations

  1. Key Laboratory of High Confidence Software Technologies (Ministry of Education), EECS, Peking University, Beijing, China

    Weijing Huang, Tengjiao Wang, Wei Chen & Yazhou Wang

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  1. Weijing Huang
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  2. Tengjiao Wang
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  3. Wei Chen
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  4. Yazhou Wang
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Corresponding author

Correspondence to Wei Chen .

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Editors and Affiliations

  1. Arizona State University , Tempe - Phoenix, Arizona, USA

    Selçuk Candan

  2. Hong Kong University of Science and Tech , Hong Kong, China

    Lei Chen

  3. Aalborg University , Aalborg, Denmark

    Torben Bach Pedersen

  4. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  5. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

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Huang, W., Wang, T., Chen, W., Wang, Y. (2017). Category-Level Transfer Learning from Knowledge Base to Microblog Stream for Accurate Event Detection. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-55753-3_4

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