TextWise has recently developed Semantic Gist® to provide intuitive semantic modeling on a large number of samples, particularly vertical text documents that often do not have classification schemes associated with them. These semantic models will automatically adapt to rapidly changing content, ensuring a high level of accuracy over time. Semantic Gist® represents a significant advance in the use of machine learning, image and speech characterization, and neural networks to attack unsupervised semantic modeling. Our patent-pending approach generates a compact representation of any text by using advanced statistical language models to identify the significant features of a document. An auto-encoder neural network encodes the features into a low-dimensionality semantic representation, and then reconstructs an approximation of the original feature vector from the semantic representation. The software highlights keywords that may be underrepresented by the semantic representation and encodes these separately as a complementary feature vector. Finally, the complementary feature vector is combined with the semantic representation to produce a Semantic Gist® that can be easily used for document indexing, matching and other applications.

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Semantic Gist®
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TextWise has recently developed Semantic Gist® to provide intuitive semantic modeling on a large
number of samples, particularly vertical text documents that often do not have classification
schemes associated with them. These semantic models will automatically adapt to rapidly changing
content, ensuring a high level of accuracy over time.
Semantic Gist® represents a significant advance in the use of machine learning, image and speech
characterization, and neural networks to attack unsupervised semantic modeling. Our patentpending approach generates a compact representation of any text by using advanced statistical
language models to identify the significant features of a document.
An auto-encoder neural network encodes the features into a low-dimensionality semantic
representation, and then reconstructs an approximation of the original feature vector from the
semantic representation. The software highlights keywords that may be underrepresented by the
semantic representation and encodes these separately as a complementary feature vector.
Finally, the complementary feature vector is combined with the semantic representation to produce
a Semantic Gist® that can be easily used for document indexing, matching and other applications.
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http://textwise.com/technology[1/10/2014 5:34:07 PM]
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