3 Ways You Can Use NLP In Your Business

You may not realize it, but there’s something that you do each and every day that even the most advanced supercomputer on Earth still struggles with: Natural language processing (NLP). While you’ve spent most of your life processing large amounts of unstructured data in the form of spoken and written word, it took computer scientists a great deal of innovation in machine learning to bring you modern comforts like Google Translate, Amazon’s Alexa, or Apple’s Siri.

You don’t have to be a tech giant to start taking advantage of NLP, one of the hottest skills on Upwork’s latest Skills Index. Here’s a look at three ways you can use NLP in your business.

Unlock the hidden potential of Big Data with NLP

Besides the obvious goal of virtual artificial intelligence (AI) companions, much of the development of NLP is thanks to the growing demands of Big Data. With so much information available on the web in the form of unstructured data such as web pages, social media posts, and search queries, there’s a big incentive to invest in NLP.

In “Natural Language Processing: Turning Words Into Data,” Tyler Keenan explains:

“An important part of the Big Data revolution has been the rise in the use of unstructured data. Thanks in large part to systems like Hadoop and Spark, we now have the ability to quickly process huge troves of unstructured data that in the past would have just been left sitting in boxes and warehouses.”

Keenan goes on to explore the key open-source tools you’ll need to dive into NLP. From the web mining module Pattern to NLTK and Stanford CoreNLP, he highlights how tokenization (the splitting of text into words and terms) can be combined with tagging and parse trees (think sentence diagrams) to create more advanced NLP applications. Read the full story…

Changing the world of business intelligence with natural language queries

Writing valid SQL queries is no easy task, but there’s a huge treasure trove of information hidden beneath the surface of the marketing dashboards a typical business intelligence (BI) tool can provide. Imagine the value added to your business if the rest of your employees could make ad-hoc queries of their own without the need for a degree in data analytics.

Better still, what if anyone could just ask a computer exactly what they want without having to write a single line of code?

In “How NLP Can Turn Questions into SQL Queries,” Keenan explains some of the challenges of working on the cutting edge of BI with NLP powered SQL Queries.

“What we’re really asking here is how to convert natural human language into valid SQL queries. By itself, [NLP] is one of the most challenging areas in AI research, and translating that question into a valid SQL query introduces a whole new layer of complexity. Still, there’ve been a number of efforts over the last few years to develop models that can do just that.”

Keenan starts off with a brief discussion on the importance of ad-hoc queries to the non-technical members of a business organization. He segways into the advantages and shortcomings of visual query builders, before taking a deep dive into the cutting edge research of Natural Language queries and finishing off with an example. Learn more by reading the full article…

Automate your translation and transcription needs with NLP

Whether it’s transcription, the conversion of spoken word into written text, or translation, the conversion of one language to another, advancements in machine learning and NLP have brought us one step closer to the communication’s Holy Grail of a universal translator. In “How NLP Is Changing Transcription and Translation,” Keenan writes about the challenges of automating these two tasks:

“Teaching a computer how to understand human language is one of the most difficult problems in computer science. Whenever we encounter written or spoken language, we naturally correct grammar or spelling mistakes, resolve ambiguities, and infer meaning from context. To a computer, these tasks represent enormous technical challenges, which is why common language-related tasks like transcription and translation have been slower to automate than many other tasks.”

NLP is the main driving force behind the huge improvements we’ve seen in automated translators and transcription software in recent years. That said, there is still room for improvement, which is why Keenan offers some tips on when to use humans or machines for your translation and transcription needs…

Eager to learn more about AI? Check out more articles about the NLP in the Hiring Headquarters >>

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