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.
As an instance of foreign language comprehension, L2 humor perception is proved to be challenging for the foreign language learners. However, the body of literature is heavier on the side of humor production than humor perception. The current study explores the extent to which Iranian English as foreign language (EFL) learners perceive different types of English humor in comparison with the English native speakers. The participants were 153 Iranian EFL learners at intermediate level of language proficiency who were randomly selected from English language learners from several English language institutes in Shiraz, Iran, and 30 American English native speakers who voluntarily participated in this study. A questionnaire consisting of six contextualized jokes of three major types of universal, cultural and linguistic (with morphological, phonological, lexical and syntactic subcategories)was developed based on Schmitz's classification of verbal humor to obtain the quantitative data. Moreover, a semi-structured interview was conducted to elicit the perception of those participants who did not find the jokes humorous. The results showed that the majority of Iranian EFL participants did not realize the humor in the jokes. Also, the findings revealed that generally speaking, Iranian EFL learners' perception of humor is significantly lower in all types of jokes examined. The best perceived type of humor was found to be the linguistic humor of morphological type for the Iranian EFL learners and the lexical type for English native speakers. It was also discovered that the phonological humor was the least perceived type of humor for both Iranian EFL learners and English native speakers.
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