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We describe the IUCL+ system for the shared task of the First Workshop on Computational Approaches to Code Switching (Solorio et al., 2014), in which participants were challenged to label each word in Twitter texts as a named entity or one of two candidate languages. Our system combines character n-gram probabilities , lexical probabilities, word label(More)
Focusing on applications for analyzing learner language which evaluate semantic appropri-ateness and accuracy, we collect data from a task which models some aspects of interaction , namely a picture description task (PDT). We parse responses to the PDT into dependency graphs with an an off-the-shelf parser, then use a decision tree to classify sentences(More)
Focusing on applications for analyzing learner language which evaluate semantic appropriate-ness and accuracy, we build from previous work which modeled some aspects of interaction, namely a picture description task (PDT), with the goal of integrating a spelling correction component in this context. After parsing a sentence and extracting semantic(More)
We describe the Indiana University system for SemEval Task 5, the L2 writing assistant task, as well as some extensions to the system that were completed after the main evaluation. Our team submitted translations for all four language pairs in the evaluation, yielding the top scores for English-German. The system is based on combining several information(More)
We investigate questions of how to reason about learner meaning in cases where the set of correct meanings is never entirely complete , specifically for the case of picture description tasks (PDTs). To operationalize this, we explore different models of representing and scoring non-native speaker (NNS) responses to a picture, including bags of dependencies(More)
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