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Title: Enhancing Search Results Relevance Using Word2Vec Language Models.

Abstract

Abstract not provided.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1401919
Report Number(s):
SAND2016-10378C
648322
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Predictive Analytics World Government held October 17-20, 2016 in Washington, DC.
Country of Publication:
United States
Language:
English

Citation Formats

Herzer, John A., and Zhang, Pengchu. Enhancing Search Results Relevance Using Word2Vec Language Models.. United States: N. p., 2016. Web.
Herzer, John A., & Zhang, Pengchu. Enhancing Search Results Relevance Using Word2Vec Language Models.. United States.
Herzer, John A., and Zhang, Pengchu. Sat . "Enhancing Search Results Relevance Using Word2Vec Language Models.". United States. doi:. https://www.osti.gov/servlets/purl/1401919.
@article{osti_1401919,
title = {Enhancing Search Results Relevance Using Word2Vec Language Models.},
author = {Herzer, John A. and Zhang, Pengchu},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Oct 01 00:00:00 EDT 2016},
month = {Sat Oct 01 00:00:00 EDT 2016}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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