Advertisement

Enhancing the Literature Review Using Author-Topic Profiling

  • Alisa Kongthon
  • Choochart Haruechaiyasak
  • Santipong Thaiprayoon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5362)

Abstract

In this paper, we utilize bibliographic data for identifying author-topic relations which can be used to enhance the traditional literature review. When writing a research paper, researchers often cite on the order of tens of references which do not provide the complete coverage of the research context especially when the targeted research is multidisciplinary. Author-topic profiling can help researchers discover a broader picture of their topic of interest including topical relationships and research community. We apply the Latent Dirichlet Allocation (LDA) to generate multinomial distributions over words and topics to discover author-topic relations from text collections. As an illustration, we apply the methodology to bibliographic abstracts related to Emerging Infectious Diseases (EIDs) research topic.

Keywords

Bibliographic data text mining Latent Dirichlet Allocation (LDA) author-topic profiling literature review 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Landauer, T., McNamara, D., Dennis, S., Kintsch, W. (eds.) Latent Semantic Analysis: A Road to Meaning. Lawrence Erlbaum, Mahwah (2006)Google Scholar
  2. 2.
    Deerwester, S., Dumais, S., Landauer, T., Furnas, G., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  3. 3.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proc. of the 22nd annual international ACM SIGIR conference, pp. 50–57 (1999)Google Scholar
  4. 4.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. Journal of Machine Learning Research, 993–1022 (2003)Google Scholar
  5. 5.
    Steyvers, M., Smyth, P., Rosen-Zvi, M., Griffiths, T.: Probabilistic Author-Topic Models for Information Discovery. In: Proc. of the 10th ACM SIGKDD international conference on Knowledge Discovery and Data mining, pp. 306–315 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alisa Kongthon
    • 1
  • Choochart Haruechaiyasak
    • 1
  • Santipong Thaiprayoon
    • 1
  1. 1.Human Language Technology Laboratory (HLT), National Electronics and Computer Technology Center (NECTEC)Thailand Science Park, Klong LuangPathumthaniThailand

Personalised recommendations