Guiding knowledge discovery through interactive data mining
Managing data mining technologies in organizations
Digital Intuition: Applying Common Sense Using Dimensionality Reduction
IEEE Intelligent Systems
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Discovering semantic relations using singular value decomposition based techniques
Discovering semantic relations using singular value decomposition based techniques
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
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In AI, we often need to make sense of data that can be measured in many different dimensions -- thousands of dimensions or more -- especially when this data represents natural language semantics. Dimensionality reduction techniques can make this kind of data more understandable and more powerful, by projecting the data into a space of many fewer dimensions, which are suggested by the computer. Still, frequently, these results require more dimensions than the human mind can grasp at once to represent all the meaningful distinctions in the data. We present Luminoso, a tool that helps researchers to visualize and understand a multi-dimensional semantic space by exploring it interactively. It also streamlines the process of creating such a space, by inputting text documents and optionally including common-sense background information. This interface is based on the fundamental operation of "grabbing" a point, which simultaneously allows a user to rotate their view using that data point, view associated text and statistics, and compare it to other data points. This also highlights the point's neighborhood of semantically-associated points, providing clues for reasons as to why the points were classified along the dimensions they were. We show how this interface can be used to discover trends in a text corpus, such as free-text responses to a survey.