Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
97 Things About Ethics Everyone In Data Science Should Know
 Every now and then an opportunity comes along that...
Machine Learning is Transforming Modern Healthcare
 The pandemic has propelled the adoption of innovation and...
It’s a Bird, It’s a Plane, It’s a Classified Flying Object
 How Computer Vision Is Used To Classify Objects. Featuring...
Overcoming the Explainability Challenges of Machine Learning Models
 Some History Machine Learning Models, which have historically been...
SHARE THIS:

4 years ago
Wise Practitioner – Text Analytics Interview Series: John Herzer and Pengchu Zhang at Sandia National Laboratories

 In anticipation of their upcoming conference co-presentation, Enhancing search results relevance using Word2Vec Language Models at Text Analytics World Chicago, June 21-22, 2016, we asked Pengchu Zhang, Computer Scientist at Sandia National Laboratories, and John Herzer, Enterprise Search Project Lead at Sandia National Laboratories, a few questions about their work in text analytics. Q: In your work with text analytics, what behavior or outcome do your models predict? A: We use the Word2Vec Neural Network model in our search application to predict word usage in our corpus for a particular context.  Word2Vec consists of two models, the Continuous Bag of Words (CBOW)

To view this content
Login OR subscribe for free

Already receive the Machine Learning Times emails?
The Machine Learning Times now requires legacy email subscribers to upgrade their subscription - one time only - in order to attain a password-protected login and gain complete access.

Click here to complete this one-time subscription upgrade

Existing Users Log In
   
New User Registration
*Required field

Comments are closed.

Pin It on Pinterest

Share This