A topic based document relevance ranking model

Gao, Yang, Xu, Yue, & Li, Yuefeng (2014) A topic based document relevance ranking model. In 23rd International World Wide Web Conference, 7-11 April 2014, Seoul, Korea.


Topic modelling has been widely used in the fields of information retrieval, text mining, machine learning, etc. In this paper, we propose a novel model, Pattern Enhanced Topic Model (PETM), which makes improvements to topic modelling by semantically representing topics with discriminative patterns, and also makes innovative contributions to information filtering by utilising the proposed PETM to determine document relevance based on topics distribution and maximum matched patterns proposed in this paper. Extensive experiments are conducted to evaluate the effectiveness of PETM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models.

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ID Code: 67263
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Relevance Ranking, Topic Modelling, Pattern Mining
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500) > Web Technologies (excl. Web Search) (080505)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Please consult the authors
Deposited On: 12 Feb 2014 02:44
Last Modified: 21 Apr 2014 06:22

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