NLP meets the cloud

NLE Blog post July 15In his latest industry watch column, Robert Dale, Chief Technology Officer for Arria NLG, takes a look at what’s on offer in the NLP microservices space, reviewing five SaaS offerings as of June 2015

Below is an extract from the column

With NLP services now widely available via cloud APIs, tasks like named entity recognition and sentiment analysis are virtually commodities. We look at what’s on offer, and make some suggestions for how to get rich.

Software as a service, or SaaS – the mode of software delivery where you pay a monthly or annual subscription to use a cloud-based service, rather than having a piece of software installed on your desktop just gets more and more popular. If you’re a user of Evernote or CrashPlan, or in fact even GMail or Google Docs, you’ve used SaaS. The biggest impact of the model is in the world of enterprise software, with applications like Salesforce, Netsuite and Concur now part of the furniture for many organisations. SaaS is big business: depending on which industry analyst you trust, the SaaS market will be worth somewhere between US$70 billion and US$120 billion by 2018. The benefits from the software vendor’s point of view are well known: you only have one instance of your software to maintain and upgrade, provisioning can be handled elastically, the revenue model is very attractive, and you get better control of your intellectual property. And customers like the hassle-free access from any web-enabled device without setup or maintenance, the ability to turn subscriptions on and off with no up-front licence fees, and not having to talk to the IT department to get what they want.

The SaaS model meets the NLP world in the area of cloud-based microservices: a specific form of SaaS where you deliver a small, well-defined, modular set of services through some lightweight mechanism. By combining NLP microservices in novel ways with other functionalities, you can easily build a sophisticated mashup that might just net you an early retirement. The economics of commercial NLP microservices offerings make these an appealing way to get your app up and running without having to build all the bits yourself, with your costs scaling comfortably with the success of your innovation. So what is out there in the NLP microservices space? That early retirement thing sounded good to me, so I decided to take a look. But here’s the thing: I’m lazy.

I want to know with minimal effort whether someone’s toolset is going to do the job for me; I don’t want to spend hours digging through a website to understand what’s on offer. So, I decided to evaluate SaaS offerings in the NLP space using, appropriately, the SAS (Short Attention Span) methodology: I would see how many functioning NLP service vendors I could track down in an afternoon on the web,

and I would give each website a maximum of five minutes of exploration time to see what it offered up. If after five minutes on a site I couldn’t really form a clear picture of what was on offer, how to use it, or what it would cost me, I would move on. Expecting me to read more than a paragraph of text is so Gen X.

Before we get into specifics, some general comments about the nature of these services are in order, because what’s striking is the similarities that hold across the different providers. Taken together, these almost constitute a playbook for rolling out a SaaS offering in this space.

Read the rest of the article including reviews of Alchemy API, TextRazor and more in the Journal of Natural Language Engineering

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