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Abstract

The authors address the problem of unsupervised ensemble ranking. Traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. Beyond the aforementioned combine-then-rank and rank-then-combine approaches, the authors propose a novel rank-learn-combine ranking framework, called Interactive Ranking (iRANK), which allows two base rankers to teach each other before combination during the ranking process by providing their own ranking results as feedback to the others to boost the ranking performance. This mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. The authors further design two ranking refinement strategies to efficiently and effectively use the feedback based on reasonable assumptions and rational analysis. Although iRANK is applicable to many applications, as a case study, they apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the DUC 2005 and 2006 data sets. The results are encouraging with consistent and promising improvements.

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Abstract

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.

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Abstract

Online health platforms play an important role in chronic disease management. Patients participate in online health platforms to receive and provide health-related support from each other. However, there remains a debate about whether the influence of social interaction on patient health anxiety is linearly positive. Based on uncertainty, information overload, and the theory of motivational information management, we develop and test a model considering a potential curvilinear relationship between social interaction and health anxiety, as well as a moderating effect of health literacy. We collect patient interaction data from an online health platform based on chronic disease management in China and use text mining and econometrics to test our hypotheses. Specifically, we find an inverted U-shaped relationship between informational provision and health anxiety. Our results also show that information receipt and emotion provision have U-shaped relationships with health anxiety. Interestingly, health literacy can effectively alleviate the U-shaped relationship between information receipt and health anxiety. These findings not only provide new insights into the literature on online patient interactions but also provide decision support for patients and platform managers.

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Date

22. 3.2013 19:50:00