Getting Started

Developers can start by downloading the code from our GitHub repository and following the instructions to install NLP Architect. A comprehensive documentation for all the core modules and end-to-end examples can be found here. We look forward to receiving feedback, feature requests or pull request contributions from all users.

Distiller

Next steps

In our previous blog, we discussed that by building a stack of NLP components based on latest DL technologies, it allows us to build foundations to tackle many applications for our partners and customers. It also enables us to continuously incorporate new results from our research and data science into the stack. In future releases, we are planning to demonstrate these advantages with solutions including sentiment extraction, topic and trend analysis, term set expansion and relation extraction. We are also researching unsupervised and semi-supervised methods that will be introduced into interpretable NLU models and domain-adaptive NLP solutions.

Acknowledgments

Credits go to our team of NLP researchers and developers at Intel AI Lab, Peter Izsak, Anna Bethke, Daniel Korat, Amit Yaccobi, Andy Keller, Jonathan Mamou, Shira Guskin, Sharath Nittur Sridhar, Oren Pereg, Alon Eirew, Sapir Tsabari, Yael Green, Chinnikrishna Kothapalli.

Notices and Disclaimers

Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
© Intel Corporation

Original article from:
https://www.intel.com/content/www/us/en/artificial-intelligence/posts/introducing-nlp-architect-by-intel-ai-lab.html?elq_cid=4948145&erpm_id=7166024

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