This paper describes the results of a study designed to assess human expert ratings of educational concept features for use in automatic core concept extraction systems. Digital library resources provided the content base for human experts to annotate automatically extracted concepts on seven dimensions: coreness, local importance, topic, content, phrasing, structure, and function. The annotated concepts were used as training data to build a machine learning classifier as part of a tool used to predict the core concepts in the document. These predictions were compared with the experts' judgment of concept coreness.
The computing research methods (CRM) literature is scattered across discourse communities and published in specialty journals and conference proceedings. This dispersion has led to the use of inconsistent terminology when referring to CRM. With no established CRM vocabulary and isolated
With all the information available on the web, there is a growing need to provide mobile access to this information for the large, growing population of mobile internet users. In this paper, we propose a solution to the problem of open web mobile information retrieval, by conducting a dialogue with the user over a simple text-based interface. Using techniques from NLP, web page analysis, and information extraction, our approach automatically navigates web sites on the user's behalf and extracts specific information from those sites to present to the user textually. Empirical evaluation shows that our approach to open web information retrieval is feasible, and a qualitative evaluation validates that such a system meets user needs for mobile information access.
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