Finding your way in a multi-dimensional semantic space with luminoso Authors: Robert H. Speer, Catherine Havasi, K. Nichole Treadway, Henry Lieberman Keywords: common sense, n-dimensional visualization, natural language processing, SVD
Abstract:
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.
I particularly like the interactive rotation about a data point.
Makes me think of rotating identifications or even within complexes of subjects.
The presentation of “rotation” I suspect to be domain specific.
The “geek” graph/node presentation probably isn’t the best one for all audiences. Open question as to what might work better.
See: Luminoso (homepage) and Luminoso (Github)