Recognizing Text Genres with Simple Metrics Using Discriminant Analysis

A simple method for categorizing texts into predetermined text genre categories using the statistical standard technique of discriminant analysis is demonstrated with application to the Brown corpus. Discriminant analysis makes it possible use a large number of parameters that may be specific for a certain corpus or information stream, and combine them into a small number of functions, with the parameters weighted on basis of how useful they are for discriminating text genres. An application to information retrieval is discussed.Comment: 6 pages, LaTeX, In proceedings of COLING 9

An online collaborative learning system : designing for evaluation of students' learning

This paper will discuss work-in-progress in the development and evaluation of an online collaborative learning system. The context is a study of a course in an on-campus weekend part-time program attended by students who share similar professional engineering backgrounds but living far apart from each other with no opportunities to meet for discussions between weekends. The course requires students to tackle problems based on real life scenarios within small online groups after having attended lectures over the weekend. The research will look at ways in which group work can be conducted, and the contribution of the instructor. The approach to be taken will be an interpretive case study using questionnaire survey, text analysis and interviews. The main findings from the study will be reported, with focus on the strengths of, and difficulties in, using the research methods

Towards a Semantic Search Engine for Scientific Articles

Because of the data deluge in scientific publication, finding relevant information is getting harder and harder for researchers and readers. Building an enhanced scientific search engine by taking semantic relations into account poses a great challenge. As a starting point, semantic relations between keywords from scientific articles could be extracted in order to classify articles. This might help later in the process of browsing and searching for content in a meaningful scientific way. Indeed, by connecting keywords, the context of the article can be extracted. This paper aims to provide ideas to build such a smart search engine and describes the initial contributions towards achieving such an ambitious goal

Twitter in Academic Conferences: Usage, Networking and Participation over Time

Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction network between users to understand whether assumptions about the structure of the conversations hold over time and between different types of interactions, such as retweets, replies, and mentions. While `people come and people go', we want to understand what keeps people stay with the conference on Twitter. By casting the problem to a classification task, we find different factors that contribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active participation among members