An event based topic learning pipeline for neuroimaging literature mining

Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods

Bittm: A core biterms-based topic model for targeted analysis

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p