Building a natural language processing library for Apache Spark

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: David Talby on a new NLP library for Spark, and why model development starts after a model gets deployed to production.

When I first discovered and started using Apache Spark, a majority of the use cases I used it for involved unstructured text. The absence of libraries meant rolling my own NLP utilities, and, in many cases, implementing a machine learning library (this was pre deep learning, and MLlib was much smaller). I’d always wondered why no one bothered to create an NLP library for Spark when many people were using Spark to process large amounts of text. The recent, early success of BigDL confirms that users like the option of having native libraries.

In this episode of the Data Show, I spoke with David Talby of Pacific.AI, a consulting company that specializes in data science, analytics, and big data. A couple of years ago I mentioned the need for an NLP library within Spark to Talby; he not only agreed, he rounded up collaborators to build such a library. They eventually carved out time to build the newly released Spark NLP library. Judging by the reception received by BigDL and the number of Spark users faced with large-scale text processing tasks, I suspect Spark NLP will be a standard tool among Spark users.

Talby and I also discussed his work helping companies build, deploy, and monitor machine learning models. Tools and best practices for model development and deployment are just beginning to emerge—I summarized some of them in a recent post, and, in this episode, I discussed these topics with a leading practitioner.

Here are some highlights from our conversation:

The state of NLP in Spark

Here are your two choices today. Either you want to leverage all of the performance and optimization that Spark gives you, which means you want to stay basically within the JVM, and you want to use a Java-based library. In which case, you have options that include OpenNLP, which is open source, or Stanford NLP, which requires licensing in order to use in a commercial product. These are older and more academically oriented libraries. So, they have limitations in performance and what they do.
Continue reading “Building a natural language processing library for Apache Spark”