BioTopic: a topic-driven biological literature mining system
by Xi Wang; Peiyan Zhu; Tao Liu; Ke Xu
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 14, No. 4, 2016

Abstract: Biology and biomedicine are flourishing disciplines, with massive biological data produced in experiments and huge amount of research papers published in journals. In such a big data context, unsupervised data mining methods such as topic models are used to extract topics from large-scale document collections. In this paper, we present a biological literature mining system based on topic modelling (BioTopic). Experiments show that the perplexity reduction percentage of our pre-processing method is 5% larger that of a traditional pre-processing method. The precision of our search performance reaches 86%, which is better that that of a unigram language model. Our method employs linguistic information from shallow parsing to better pre-process biological literature for topic models. BioTopic with fine-grained pre-processing and topic modelling works better than traditional literature mining systems.

Online publication date: Wed, 06-Apr-2016

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