A topic based document relevance ranking model

Topic modelling has been widely used in the fields of in- formation 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 discrim- inative patterns, and also makes innovative contributions to information filtering by utilising the proposed PETM to de- Termine document relevance based on topics distribution and maximum matched patterns proposed in this paper. Exten- sive 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 signifi- cantly outperforms both state-of-the-art term-based models and pattern-based models.