Google Awards Grants For Natural Language Research
Written by Alex Armstrong   
Saturday, 06 July 2013

Google has awarded over $1.2 million to support research in several areas of several natural language understanding that relate to Google's concept of the Knowledge Graph.

Google has been investing heavily in machine learning and deep neural networks to improve web search. Supporting natural language understanding is also motivated by the need to further search technology.

In the announcement of the awards Google Research Blog explains how natural language processing is integral to its Knowledge Graph technology that represents a shift "from strings to things", stating:

Understanding natural language is at the core of Google's work to help people get the information they need as quickly and easily as possible. At Google we work hard to advance the state of the art in natural language processing, to improve the understanding of fundamental principles, and to solve the algorithmic and engineering challenges to make these technologies part of everyday life. Language is inherently productive; an infinite number of meaningful new expressions can be formed by combining the meaning of their components systematically. The logical next step is the semantic modeling of structured meaningful expressions -- in other words, “what is said” about entities. We envision that knowledge graphs will support the next leap forward in language understanding towards scalable compositional analyses, by providing a universe of entities, facts and relations upon which semantic composition operations can be designed and implemented.

The research topic that have been awarded grants range from semantic parsing to statistical models of life stories and novel compositional inference and representation approaches to modeling relations and events in the Knowledge Graph.

The recipients are:

 

  • Mark Johnson and Lan Du (Macquarie University) and Wray Buntine (NICTA) for “Generative models of Life Stories”
  • Percy Liang and Christopher Manning (Stanford University) for “Tensor Factorizing Knowledge Graphs”
  • Sebastian Riedel (University College London) and Andrew McCallum (University of Massachusetts, Amherst) for “Populating a Knowledge Base of Compositional Universal Schema”
  • Ivan Titov (University of Amsterdam) for “Learning to Reason by Exploiting Grounded Text Collections”
  • Hans Uszkoreit (Saarland University and DFKI), Feiyu Xu (DFKI and Saarland University) and Roberto Navigli (Sapienza University of Rome) for “Language Understanding cum Knowledge Yield”
  • Luke Zettlemoyer (University of Washington) for “Weakly Supervised Learning for Semantic Parsing with Knowledge Graphs”googlelogo

More Information

Natural Language Understanding-focused awards announced

Related Articles

Google Search Goes Semantic - The Knowledge Graph

Google Uses AI To Find Good Tables

Google Explains How AI Photo Search Works

Deep Learning Researchers To Work For Google

 

To be informed about new articles on I Programmer, install the I Programmer Toolbar, subscribe to the RSS feed, follow us on, Twitter, Facebook, Google+ or Linkedin,  or sign up for our weekly newsletter.

 

espbook

 

Comments




or email your comment to: comments@i-programmer.info

 

Banner


Sequin - Open Source Message Stream Built On Postgres
31/10/2024

Sequin is a tool for capturing changes and streaming data out of your Postgres database, guaranteeing exactly once processing. What does that mean?



Pico 2W Announced But There Is A Surprise!
25/11/2024

Raspberry Pi released the Pico 2 a few months ago and we have been waiting for the Pico 2W since then. But Pimoroni beat them to the draw with the Pico Plus 2W based on the RM2 radio module and hinted [ ... ]


More News

 

Last Updated ( Saturday, 06 July 2013 )