CoMiC: Exploring Text Segmentation and Similarity in the English Entrance Exams Task
نویسندگان
چکیده
This paper describes our contribution to the English Entrance Exams task of CLEF 2015, which requires participating systems to automatically solve multiple choice reading comprehension tasks. We use a combination of text segmentation and different similarity measures with the aim of exploiting two observed aspects of tests: 1) the often linear relationship between reading text and test questions and 2) the differences in linguistic encoding of content in distractor answers vs. the correct answer. Using features based on these characteristics, we train a ranking SVM in order to learn answer preferences. In the official 2015 competition we achieve a c@1 score of 0.29, a medium but encouraging result. We identify two main issues that pave the way towards further research.
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