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This Presentation shows schools how to have software applications that will enhance service delivery
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Information extraction researchers have recently recognized that more subtle information beyond the basic semantic content of a message can be communicated via linguistic features in text, such as sentiments, emotions, perspectives, and intentions. One way to describe this information is that it represents something about the generator’s mental state, which is often interpreted as the tone of the message. A current technical barrier to developing a general-purpose tone identification system is the lack of reliable training data, with messages annotated with the message tone. We first describe a method for creating the necessary annotated data using human-based computation, based on interactive games between humans trying to generate and interpret messages conveying different tones. This draws on the use of game with a purpose methods from computer science and wisdom of the crowds methods from cognitive science. We then demonstrate the utility of this kind of database and the advantage of human-based computation by examining the performance of two machine learning classifiers trained on the database, each of which uses only shallow linguistic features. Though we already find near-human levels of performance with one classifier, we also suggest more sophisticated linguistic features and alternate implementations for the database that may improve tone identification results further.
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Information extraction researchers have recently recognized that more subtle information beyond the basic semantic content of a message can be communicated via linguistic features in text, such as sentiments, emotions, perspectives, and intentions. One way to describe this information is that it represents something about the generator’s mental state, which is often interpreted as the tone of the message. A current technical barrier to developing a general-purpose tone identification system is the lack of reliable training data, with messages annotated with the message tone. We first describe a method for creating the necessary annotated data using human-based computation, based on interactive games between humans trying to generate and interpret messages conveying different tones. This draws on the use of game with a purpose methods from computer science and wisdom of the crowds methods from cognitive science. We then demonstrate the utility of this kind of database and the advantage of human-based computation by examining the performance of two machine learning classifiers trained on the database, each of which uses only shallow linguistic features. Though we already find near-human levels of performance with one classifier, we also suggest more sophisticated linguistic features and alternate implementations for the database that may improve tone identification results further.
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