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We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative(More)
Semantic orientation (SO) for texts is often determined on the basis of the positive or negative polarity, or sentiment, found in the text. Polarity is typically extracted using the positive and negative words in the text, with a particular focus on adjectives, since they convey a high degree of opinion. Not all adjectives are created equal, however.(More)
Despite the potential to dominate radiology reporting, current speech recognition technology is thus far a weak and inconsistent alternative to traditional human transcription. This is attributable to poor accuracy rates, in spite of vendor claims, and the wasted resources that go into correcting erroneous reports. A solution to this problem is(More)
Our students are the social media generation, touting Facebook, Google+, and even Twitter accounts as a matter of course. Providing rich, highly integrated environments, social media systems are a template for community and connection. In contrast, CS education is via singular modalities: lectures, textbooks, labs, discussions, et cetera, that share no(More)
CPU architecture is a complex field in which students can be easily overwhelmed. To mitigate this complexity we demonstrate the "Paper CPU", an in-class activity wherein students simulate a Y86 processor on paper. Students report the activity to be an interesting and useful introduction to CPU architecture, and appear to focus more on the behaviour of the(More)
Fast and accurate, non-linear autoassociators perform well in the face of unbalanced data sets, where few to no positive examples are present. In cancer diagnosis, for example, this can be convenient if only benign data is available, or if only a very small proportion of malignant data is available. As proof of concept, we apply a non-linear autoassociator(More)
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