David Barbella

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Support vector machines are a valuable tool for making classifications, but their black-box nature means that they lack the natural explanatory value that many other classifiers possess. Alternatively, many popular websites have shown recent success in explaining recommendations based on behavior of other users. Inspired by these ideas, we suggest two novel(More)
Word sense disambiguation is an important problem in learning by reading. This paper introduces analogical word-sense disambiguation, which uses human-like analogical processing over structured, relational representations to perform word sense disambiguation. Cases are automatically constructed using representations produced via natural language analysis of(More)
Social networks support efficient decentralized search: people can collectively construct short paths to a specified target in the network. Rank-based friendship—where the probability that person u befriends person v is inversely proportional to the number of people who are closer to u than v is—is an empirically validated model of acquaintanceship that(More)
Support vector machines are valuable for making classifications, but they lack the natural explanatory capability that many other classifiers possess. We suggest two methods for providing insight into support vector machine classifications. In the first, we report the support vectors most influential in the final classification for a particular test point.(More)
This paper explores the close relationship between question answering and machine reading, and how the active use of reasoning to answer (and in the process, disambiguate) questions can also be applied to reading declarative texts, where a substantial proportion of the text’s contents is already known to (represented in) the system. In question answering, a(More)
This thesis explores a communication method that is relevant to learning by reading systems and educational software systems: instructional analogy. It is widely recognized that analogical reasoning plays a vital role in our ability to detect similarities and differences and to transfer knowledge between topics. Building software that can understand these(More)
One of the original motivations for qualitative reasoning was to capture the informal, intuitive notions about the continuous world that we all share, learned via a combination of experience and culture. For example, prior research suggests that qualitative dynamics can play an important role in natural language semantics. However, the constraints of(More)
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