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Data forms an integral part of every business or organization which is used to make valuable decisions based on changing circumstances. Natural Language Processing (NLP) is a widely adopted technique used by machines to understand and communicate with humans in human language. This enables human to access, analyze and extract data more intelligently from a huge amount of unstructured data.

Intel AI Lab’s team of NLP researchers and developers has introduced NLP Architect, a new open-source Python library. This library can be used as a platform for future research and developing the state-of-the-art deep learning techniques for natural language processing and natural language understanding.

Rapid and recent advancements in deep learning and neural network paradigms has led to the growth in NLP domain. This new library offers flexibility in implementing NLP solutions which are packed with the past and ongoing NLP research and development work of Intel AI Lab.

NLP Architect overview

The current version of NLP Architect offers noteworthy features which form the backbone in terms of research and practical development. All the following models are provided with required training and inference processes:


  • It consists of NLP core models such as BIST and NP chunker that allows powerful extraction of linguistic features for NLP workflow
  • NLU models such as intent extraction (IE), name entity recognition (NER) used for intent-based applications
  • It consists of modules which address semantic understanding
  • Now consists of components which hold a key for conversational AI such as chatbot applications, dialog applications and more
  • End-to-end deep learning applications such as Q&A, reading comprehension and more

NLP Architect Overview

Source: AI Intel Blog

This library of NLP components provides the required functionality to extend NLP solutions with a range of audience. It provides excellent media for analysis and optimization of Intel software and hardware on NLP workloads.

In addition to these models, new features such as data pipelines, common functional calls, and utilities related to NLP domain which are majorly used when deploying models, are added.

To know more about the updates, you can refer the official Intel AI blog.

How NLP Architect can be used

  • You can train models using the provided datasets, configurations and algorithms
  • You can train models based on your own data
  • You can create new models or extend your existing models
  • You can explore various common and not-so-common challenges faced in NLP domain using deep learning models
  • You can optimize and extend the use of state-of-the-art deep learning algorithms
  • You can integrate various modules and utilities from the library to NLP solutions

Deep learning frameworks support

This repository supports several open source deep learning frameworks such as:

Note: We can expect the list of models to update in future. All these models will run with Python 3.5+

If you want to download the open-source Python library or want to contribute to the project by providing valuable feedback, download the code from Github. A complete documentation for all core modules with end-to-end examples can be found in their official page.

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