Mining meaningful topics from massive biomedical literature
Peiyan Zhu, Junhui Shen, Dezhi Sun, Ke Xu · 2014
There is huge amount of biomedical and biological literature online or in digital libraries. Moreover, new research papers are published with an exponential growth in recent years. So it is pressing and challenging to mine meaningful topics from massive biomedical literature. The mined topics are helpful to researchers for literature exploration and topic discovery. However, latent topics inferred by traditional topic models are not always coherent and meaningful. In this work, we propose a new methodology to mine meaningful biomedical topics with a combination of several off-the-shelf text mining techniques such as part-of-speech tagging, base noun phrase chunking, K-means clustering and latent Dirichlet allocation, which endow our methodology with scalability and implementation simplicity. We conduct comprehensive experiments on a dataset collected from PubMed. The experimental results demonstrate that our method significantly outperforms a baseline method. We also perform a qualitative analysis and present meaningful biomedical topics and multi-word expressions.