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August 09, 2022

15:00 UTC   Start Times Around the World

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Description

How to perform in-database text analytics using a pre-built ML model
Did you know that you can perform document similarity, topic modeling, and term/document classification in-database using a pre-built machine learning model that’s based on millions of Wikipedia articles? This pre-built model was produced using the Explicit Semantic Analysis (ESA) algorithm. ESA models do not find latent features, but rather use explicit features represented in the knowledge base, which in this case is a selected set of Wikipedia articles. Preparing data and training such a model can be involved, so the model being pre-built streamlines that effort. We’ll be demonstrating using the Wikipedia model through the soon-to-be-released OML4R interface on Oracle Autonomous Database. Using OML4R, we show how to create a model proxy object for this pre-build Wiki model and use it for document similarity and document classification use cases.

Your Experts

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    Miles Novotny

    Miles Novotny

    I am a Solution Engineer working out of the North America Specialist Hub who specializes in analytics solutions and has a strong interest in machine learning.
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    Sherry LaMonica

    Sherry LaMonica

    Sherry is a member of the Oracle Machine Learning Product Management team. She has 20 years of software experience focused on enabling the commercial use of the open-source data analysis software systems R and Python for data science and machine learning projects. She has worked with customers in fields as diverse as as pharmaceutical research, financial analysis, manufacturing and healthcare IT.
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    Mark Hornick

    Mark Hornick   

    Mark Hornick is the Senior Director of Product Management for the Oracle Machine Learning (OML) family of products. He leads the OML PM team and works closely with Product Development on product strategy, positioning, and evangelization. Mark has over 20 years of experience with integrating and leveraging machine learning with Oracle technologies, and working with internal and external customers in applying Oracle’s machine learning technologies for scalable and deployable data science projects. Mark is Oracle’s representative to the R Consortium and an Oracle adviser and founding member of the Analytics and Data Oracle User Community. He holds a bachelor's degree from Rutgers University and a master's degree from Brown University, both in Computer Science.
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