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.
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.
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.
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