Enhancing the Literature Review Using Author-Topic Profiling

Authors:
Alisa Kongthon;Choochart Haruechaiyasak;Santipong Thaiprayoon
Affiliations:
Human Language Technology Laboratory (HLT), National Electronics and Computer Technology Center (NECTEC), Thailand Science Park, Klong Luang, Pathumthani, Thailand 12120;Human Language Technology Laboratory (HLT), National Electronics and Computer Technology Center (NECTEC), Thailand Science Park, Klong Luang, Pathumthani, Thailand 12120;Human Language Technology Laboratory (HLT), National Electronics and Computer Technology Center (NECTEC), Thailand Science Park, Klong Luang, Pathumthani, Thailand 12120
Venue:
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Year:
2008

Citing 3
Cited 0

Probabilistic latent semantic indexing

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Latent dirichlet allocation

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Probabilistic author-topic models for information discovery

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

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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.