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Text wise technology textwise company, llc

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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 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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Text wise technology textwise company, llc

  1. 1. TextWise Technology | TextWise Company, LLC Home Services Solutions Blog Using Our API API Login About Us ABOUT US TextWise Technology Technology Semantic Gist® Whitepaper In the Media 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. Copyright © 2014 TextWise Company, LLC http://textwise.com/technology[1/10/2014 5:34:07 PM] Site Map Terms & Conditions Privacy Policy Facebook Twitter History IP Portfolio Profile           Contact Us

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