Bipartite Graph for Topic Extraction
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4363-4363.
Abstract
This paper presents a bipartite graph propagation method to be applied to different tasks in the machine learning unsupervised domain, such as topic extraction and clustering. We introduce the objectives and hypothesis that motivate the use of graph based method, and we give the intuition of the proposed Bipartite Graph Propagation Algorithm. The contribution of this study is the development of new method that allows the use of heuristic knowledge to discover topics in textual data easier than it is possible in the traditional mathematical formalism based on Latent Dirichlet Allocation (LDA). Initial experiments demonstrate that our Bipartite Graph Propagation algorithm return good results in a static context (offline algorithm). Now, our research is focusing on big amount of data and dynamic context (online algorithm).
@inproceedings{ijcai2015p629,
title = {Bipartite Graph for Topic Extraction},
author = {Faleiros, Thiago de Paulo and Lopes, Alneu de Andrade},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {4363--4363},
year = {2015},
month = {7},
note = {Doctoral Consortium},
url = {https://ijcai.org/proceedings/2015/629},
}
TY - CPAPER TI - Bipartite Graph for Topic Extraction AU - Faleiros, Thiago de Paulo AU - Lopes, Alneu de Andrade T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 4363 EP - 4363 UR - https://ijcai.org/proceedings/2015/629 ER -Copied
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