@inproceedings{aa3d9a23d7504d249f117a05c342aa4b,
title = "Graph-based model for topic detection",
abstract = "In this paper, a novel graph-based model (GBM) is proposed for topic detecting. Different from existing statistical methods, our proposed model considers more semantic factors which combines named entity and dependency relation between words derived from a dependency parse tree. In our model, a graph is constructed for representing words and their association. By utilizing spectral clustering algorithm, we get clusters of words, each cluster represents a topic respectively. Our contribution includes as follows: modeling the topic detection problem as a graph-partitioning problem; proposing a new method of ranking the words association, and based on that, the document collection is represented as an undirected weighted graph. The performance of experiment task for dimensionality reduction and text classification indicates the feasibility and potentiality of our method.",
keywords = "Dimensionality reduction, Graph partitioning, Text classification, Topic model, Words association",
author = "Ang Zhao and Xin Lin and Jing Yang",
note = "Publisher Copyright: {\textcopyright}2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014.All right reserved.; 2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014 ; Conference date: 21-07-2014 Through 24-07-2014",
year = "2014",
language = "英语",
series = "Proceedings of the 2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014",
publisher = "CSREA Press",
pages = "548--554",
editor = "Arabnia, \{Hamid R.\} and \{de la Fuente\}, David and Kozerenko, \{Elena B.\} and LaMonica, \{Peter M.\} and Liuzzi, \{Raymond A.\} and Olivas, \{Jose A.\} and Todd Waskiewicz and George Jandieri and Solo, \{Ashu M.G.\} and Tinetti, \{Fernando G.\}",
booktitle = "Proceedings of the 2014 International Conference on Artificial Intelligence, ICAI 2014 - WORLDCOMP 2014",
}