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Topic Detection with Locally Weighted Semi-supervised Collective Learning

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 10570)

Abstract

Topic detection and tracking (TDT) under modern media circumstances has been dramatically innovated with the ever-changing social network and some of the inconspicuous connections among participants in the internet communities. Instead of only considering the varied word features of analysing materials, detecting and tracking topics in multi-relational data with incidental information becomes a new trend for prevalent topic models, for example, the use of link structures and time series. In this paper, we employ the users’ groups extracted from Twitter as the social context that accompanied the corresponding news articles and explore the interior links among data points to develop the non-negative factorization methods with semi-supervised information. A locally weighted scheme is applied to original data points to differentiate the proximity of approximate points for a better approximation. We evaluate our proposed method on synthetic data set as well as real news data set combining social information extracted from Twitter. The experimental results show the performance improvement of our method comparing to other baseline methods.

Keywords

  • Non-negative matrix factorization
  • Locally weighted
  • Social context

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Notes

  1. 1.

    http://www.itl.nist.gov/iad/mig/tests/tdt/1998/.

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Acknowledgement

This work is supported in part by National Key fundamental Research and Development Program of China (No. 2013CB329601, No. 2013CB329604, No. 2013CB329606), National Natural Science Foundation of China (No. 61502517, No. 61372191, No. 61572492) and Australia Research Council Project (DP140100841). This work is also funded by the major pre-research project of National University of Defense Technology.

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Wang, Y., Quan, Y., Zhou, B., Zhang, Y., Peng, M. (2017). Topic Detection with Locally Weighted Semi-supervised Collective Learning. In: , et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10570. Springer, Cham. https://doi.org/10.1007/978-3-319-68786-5_45

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  • DOI: https://doi.org/10.1007/978-3-319-68786-5_45

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