Cognition-based approaches for high-precision text mining

This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in. Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital and connected world, cognitive approaches enable substantial opportunities in knowledge discovery. The generation and storage of electronic text is ubiquitous, hence opportunities for improved knowledge discovery span virtually all knowledge domains. While cognition-based search offers superior approaches, challenges exist due to the need to mimic, even in the most rudimentary way, the extraordinary powers of human cognition. This research addresses these challenges in the key area of a cognition-based approach to automated concept recognition. In addition it resulted in a semantic processing system framework for use in applications in any knowledge domain. Confabulation theory was applied to the problem of automated concept recognition. This is a relatively new theory of cognition using a non-Bayesian measure, called cogency, for predicting the results of human cognition. An innovative distance measure derived from cogent confabulation and called inverse cogency, to rank order candidate concepts during the recognition process. When used with a multilayer perceptron, it improved the precision of concept recognition by 5% over published benchmarks. Additional precision improvements are anticipated. These research steps build a foundation for cognition-based, high-precision text mining. Long-term it is anticipated that this foundation enables a cognitive-based approach to automated ontology learning. Such automated ontology learning will mimic human language cognition, and will, in turn, enable the practical use of cognitive-based approaches in virtually any knowledge domain --Abstract, page iii

Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes

In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10

Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach

Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context

Discovering Design Principles for Health Behavioral Change Support Systems: A Text Mining Approach

Behavioral Change Support Systems (BCSSs) aim to change users’ behavior and lifestyle. These systems have been gaining popularity with the proliferation of wearable devices and recent advances in mobile technologies. In this article, we extend the existing literature by discovering design principles for health BCSSs based on a systematic analysis of users’ feedback. Using mobile diabetes applications as an example of Health BCSSs, we use topic modeling to discover design principles from online user reviews. We demonstrate the importance of the design principles through analyzing their existence in users’ complaints. Overall, the results highlight the necessity of going beyond the techno-centric approach used in current practice and incorporating the social and organizational features into persuasive systems design, as well as integrating with medical devices and other systems in their usage context

Information Retrieval Systems Adapted to the Biomedical Domain

The terminology used in Biomedicine shows lexical peculiarities that have required the elaboration of terminological resources and information retrieval systems with specific functionalities. The main characteristics are the high rates of synonymy and homonymy, due to phenomena such as the proliferation of polysemic acronyms and their interaction with common language. Information retrieval systems in the biomedical domain use techniques oriented to the treatment of these lexical peculiarities. In this paper we review some of the techniques used in this domain, such as the application of Natural Language Processing (BioNLP), the incorporation of lexical-semantic resources, and the application of Named Entity Recognition (BioNER). Finally, we present the evaluation methods adopted to assess the suitability of these techniques for retrieving biomedical resources.Comment: 6 pages, 4 table