This paper presents a structured environment for Computer-Supported Collaborative Argumentation, which we call the Argumentative Learning Experience (ALEX). The system aims to improve understanding of argumentation and to widen and deepen the space of debate among 16-18-year-old students. To use ALEX users make arguments by selecting and completing partial sentences. An automatically created visual representation of the argument is displayed and personalised advice on the argumentation is provided to each user.
We describe a method for generating accurate, compact, human understandable text classifiers. Text datasets are indexed using Apache Lucene and Genetic Programs are used to construct Lucene search queries. Genetic programs acquire fitness by producing queries that are effective binary classifiers for a particular category when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from classification tasks.
Abstract. We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications.
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