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Authors: Yishu Miao 1 ; Chunping Li 1 ; Hui Wang 2 and Lu Zhang 1

Affiliations: 1 Tsinghua University, China ; 2 University of Ulster, United Kingdom

ISBN: 978-989-8565-29-7

Keyword(s): Hierarchical Dirichlet Process, Topic Modelling, Wikipedia, Temporal Analysis, News.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Text and Semi-Structured Data ; Soft Computing ; Symbolic Systems

Abstract: The current affairs people concern closely vary in different periods and the evolution of trends corresponds to the reports of medias. This paper considers tracking trends by incorporating non-parametric Bayesian approaches with temporal information and presents two topic modelling methods. One utilizes an infinite temporal topic model which obtains the topic distribution over time by placing a time prior when discovering topics dynamically. In order to better organize the event trend, we present another progressive superposed topic model which simulates the whole evolutionary processes of topics, including new topics’ generation, stable topics’ evolution and old topics’ vanishment, via a series of superposed topics distribution generated by hierarchical Dirichlet process. Both of the two approaches aim at solving the real-world task while avoiding Markov assumption and breaking the number limitation of topics. Meanwhile, we employ Wikipedia based semantic background knowledge to impr ove the discovered topics and their readability. The experiments are carried out on the corpus of BBC news about American Forum. The results demonstrate better organized topics, evolutionary processes of topics over time and model effectiveness. (More)

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Paper citation in several formats:
Miao, Y.; Li, C.; Wang, H. and Zhang, L. (2012). Infinite Topic Modelling for Trend Tracking - Hierarchical Dirichlet Process Approaches with Wikipedia Semantic based Method.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 35-44. DOI: 10.5220/0004133300350044

@conference{kdir12,
author={Yishu Miao. and Chunping Li. and Hui Wang. and Lu Zhang.},
title={Infinite Topic Modelling for Trend Tracking - Hierarchical Dirichlet Process Approaches with Wikipedia Semantic based Method},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={35-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004133300350044},
isbn={978-989-8565-29-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Infinite Topic Modelling for Trend Tracking - Hierarchical Dirichlet Process Approaches with Wikipedia Semantic based Method
SN - 978-989-8565-29-7
AU - Miao, Y.
AU - Li, C.
AU - Wang, H.
AU - Zhang, L.
PY - 2012
SP - 35
EP - 44
DO - 10.5220/0004133300350044

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