ML Anthology

Conference Mining via Generalized Topic Modeling

Ali Daud, Juanzi Li, Lizhu Zhou, Faqir Muhammad
ECML-PKDD 2009 pp. 244-259
doi:10.1007/978-3-642-04180-8_33 /ecmlpkdd/2009/daud2009ecmlpkdd-conference/

Abstract

Conference Mining has been an important problem discussed these days for the purpose of academic recommendation. Previous approaches mined conferences by using network connectivity or by using semantics-based intrinsic structure of the words present between documents (modeling from document level (DL)), while ignored semantics-based intrinsic structure of the words present between conferences. In this paper, we address this problem by considering semantics-based intrinsic structure of the words present in conferences (richer semantics) by modeling from conference level (CL). We propose a generalized topic modeling approach based on Latent Dirichlet Allocation (LDA) named as Conference Mining (ConMin). By using it we can discover topically related conferences, conferences correlations and conferences temporal topic trends. Experimental results show that proposed approach significantly outperformed baseline approach in discovering topically related conferences and finding conferences correlations because of its ability to produce less sparse topics.

PDF ECML-PKDD Semantic Scholar

Cite

Text

Daud et al. "Conference Mining via Generalized Topic Modeling." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04180-8_33

Markdown

[Daud et al. "Conference Mining via Generalized Topic Modeling." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/daud2009ecmlpkdd-conference/) doi:10.1007/978-3-642-04180-8_33

BibTeX

@inproceedings{daud2009ecmlpkdd-conference,
  title     = {{Conference Mining via Generalized Topic Modeling}},
  author    = {Daud, Ali and Li, Juanzi and Zhou, Lizhu and Muhammad, Faqir},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2009},
  pages     = {244-259},
  doi       = {10.1007/978-3-642-04180-8_33},
  url       = {https://mlanthology.org/ecmlpkdd/2009/daud2009ecmlpkdd-conference/}
}