Christopher T Harding

US Patent:

20200175360, Jun 4, 2020

Filed:

Nov 29, 2018

Appl. No.:

16/204408

Inventors:

- Armonk NY, US
Stephen Warren - Jopewell Junction NY, US
Rajesh Bordawekar - Millwood NY, US
Jose Neves - Poughkeepsie NY, US
Christopher Harding - Port Jervis NY, US

International Classification:

G06N 3/08
G06K 9/62
G06F 16/28
G06F 16/33
G06F 16/22
G06N 3/04

Abstract:

Methods, systems and computer program products for updating a word embedding model are provided. Aspects include receiving a first data set comprising a relational database having a plurality of words. Aspects also include generating a word embedding model comprising a plurality of word vectors by training a neural network using unsupervised machine learning based on the first data set. Each word vector of the plurality of word vector corresponds to a unique word of the plurality of words. Aspects also include storing the plurality of word vectors and a representation of a hidden layer of the neural network. Aspects also include receiving a second data set comprising data that has been added to the relational database. Aspects also include updating the word embedding model based on the second data set and the stored representation of the hidden layer of the neural network.

US Patent:

20200175390, Jun 4, 2020

Filed:

Nov 29, 2018

Appl. No.:

16/204486

Inventors:

- Armonk NY, US
Rajesh Bordawekar - Westchester NY, US
Stephen Warren - Hopewell Junction NY, US
Christopher Harding - Port Jervis NY, US
Jose Neves - Poughkeepsie NY, US

International Classification:

G06N 5/04
G06N 3/08
G06F 17/27
G06K 9/62
G06N 20/10
G06F 11/36

Abstract:

Methods, systems and computer program products for determining recommended parameters for use in generating a word embedding model are provided. Aspects include storing a plurality of meaningful test cases. Each meaningful test case includes a test data profile and one or more test model parameters used to create a word embedding model that has been classified as yielding meaningful results. Aspects include receiving a production data set to be used in generating a new word embedding model. The production data set includes data stored in a relational database having a plurality of columns and a plurality of rows. Aspects include generating a data profile associated with the production data set. Aspects include generating a recommendation for one or more production model parameters for use in building a word embedding model based on the data profile associated with the production data set and the plurality of meaningful test cases.