Using Spark NLP with Jupyter notebook for natural language processing in Docker environment. As described in , Docker is a tool that allows us to easily deploy applications (e.g., Spark NLP) in a sandbox (called container) to run on any Docker supported host operating system (i.e., Mac).
As described in , natural language processing (NLP) is a common research subfield shared by many research fields such as linguistics, computer science, information engineering, and artificial intelligence, etc. NLP is concerned with the interactions between computers and human natural languages in general and in particular how to use computers to process and analyze natural language data (e.g., text, voice, etc.). Some of the major challenges in NLP include speech recognition, natural language understanding (e.g., text understanding), and natural language generation.
One of the early applications of machine learning in text understanding is email and message spam detection . With the advancement of deep learning, many new advanced language understanding methods have been published such as the deep learning method BERT (see  for an example of using MobileBERT for question and answer).
The other popular method in NLP is Named Entity Recognition (NER). The main purpose of NER is to extract named entities (e.g., personal names, organization names, location names, product names, etc.) from unstructured text. There are many open source NLP libraries/tools with NER support such as NLTK and SpaCy . Recently Spark NLP  gets more and more attention due to its more complete list of supported NLP features .
It seems to me that the development of Spark NLP  is based on Ubuntu Linux and OpenJDK. Thus it’s straight forward to setup environment for Spark NLP in Colab (see instructions and code examples) since Colab uses Ubuntu operating system. However, I noticed that it’s difficult to set up a local environment on Mac for Spark NLP due to the following known exception:
To avoid the problem, this article demonstrates how to set up a Docker environment  to run Spark NLP for NER and other NLP features in a Docker container. Such a Docker environment can serve as a basis for establishing a Spark NLP microservices platform.
As described in , Docker is a tool that allows us to easily deploy applications (e.g., Spark NLP) in a sandbox (called container) to run on any Docker supported host operating system (i.e., Mac).
The basic concepts of Docker are:
A Dockerfile  is a simple text file that contains a list of commands (similar to Linux commands) for creating a Docker image. It’s a way to automate the Docker image creation process.
A docker image  is a read-only template that contains a set of instructions for creating a Docker container that can run on the Docker platform. It provides a convenient way to package up applications and preconfigured server environments.
A Docker image is built from a Dockerfile.
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