Natural Language Processing — Is It Just Counting Token Occurrences and Noun Phrases?

Natural language processing, let’s be clear about that, is booming. Because this statistical discipline is part of the greater realm of Machine Learning and Artificial Intelligence, and the two latter have exploded in popularity recently, it is only natural if (no pun intended) natural language processing is also on the rise.

  • On 14/01/2018 by @lazharichir
Natural Language Processing — Is It Just Counting Token Occurrences and Noun Phrases?

Natural language processing, let’s be clear about that, is booming. Because this statistical discipline is part of the greater realm of Machine Learning and Artificial Intelligence, and the two latter have exploded in popularity recently, it is only natural if (no pun intended) natural language processing is also on the rise.

Not a day passes without a new startup coming up with a new revolutionary product to build chatbots, smart IoT devices, support dispatch, and so on. Text understanding is crucial for most business domains so it does make sense if everybody wants a drop from this huge ocean. However, precipitation is hurting most of these products: stating how much you are using natural language processing in the core of your top product doesn’t make your product de facto revolutionary and great at what it is meant to do.

Buzzwords have always plagued businesses and corrupted real technological advances and discoveries. It is tempting to jump on the bandwagon and put a hyped-up label on what you are doing, but it comes to a point where it hurts more than it helps.

While we are working hard, every single day, to fine tune our multiple algorithms, we find it irritating (to say the list) when we see companies talking about natural language processing for counting noun phrases in a blob of text. Sure, we also count that, but that’s not the finality of our algorithms. It’s just one step amongst hundreds of complicated and intricate functions.

As a bootstrapped company, I wrote thousands of different functions to get to where we are at today. Then I deleted them all. Rewrote them. Deleted them again. And rewrote it all. Because each time I was bringing improvements to the whole framework.

By using Google Cloud Natural Language API, we aren’t delegated the hard-work to them but instead, we use them to confirm the accuracy of our discoveries and assessments. If what you find is confirmed by Google, the company that understands text the most, then you are on the right path.

Anyway, these are just some thoughts I had after browsing Twitter and seeing startups with products an Excel spreadsheet could replace. I just want all of us to use NLP to create helpful, if not revolutionary, applications that will make the life of our end-user so much better. At work, at home, or on the go. At TopicSeed, we chose to serve content marketers in helping them writing about topics that convert by either broadening or narrowing future articles.