Welcome to our new series called Search for Marketers. In this sequence of blogs, you will learn the ins and outs of search from basics to how it changes our lives. If you’re wondering how algorithms, processing tools and humans impact search, you’re in the right place. This series will break down each search element, whether you want to understand search as a foundation for creating content or want to know how search engines give you accurate results.

Let’s kick things off with natural language processing. “Ask Jeeves” was one of the first natural language processing search engines in which users were called to ask full questions instead of keywords. Now, search engines, voice search and digital assistants all use natural language processing.

Google characterized the process in a college humor video mocking search habits.

What is natural language processing?

Natural language processing understands colloquial, everyday language and phrasing as if you were talking to another person. It’s a combination of computer science, algorithms, and artificial intelligence. Computers are taught and programmed to process natural language data to make humans’ lives easier. Natural language processing is capable of making decisions based on syntax, semantics, and pragmatics.

People want a quick answer after one search. As we said in our previous blog post, digital audiences have shorter attention spans than goldfish, especially on mobile. Natural language processing shortens the time it takes to answer a query because they can just ask as if they were asking a person in front of them.

Keywords v. natural language

Initially, search engines required keywords in search queries. A list of words that relate to our problem, rather than a phrase or question. Keyword-based search eliminates filler words such as “the” or “is.” It isn’t constructive or productive.

Let’s say you want to know how many tweets included #contentmarketing.  With keyword-based search, you might search “content marketing”. This combination of keywords brings up what is content marketing articles but doesn’t give you the exact answer.

However, with natural language processing, you can ask “How many tweets include content marketing hashtags” The algorithm translates your question and understands that it must find the number of tweets around #contentmarketing. It presents you with “how to find popular hashtags on twitter”.

Natural language understanding

Natural language understanding is supplementary to natural language processing. It goes into the decision-making of the natural language processing. Natural language understanding is the actual act of the computer, algorithm or artificial intelligence reading and comprehending language. It teaches computers how to translate human language to its language of zeros and ones – computers are multilingual.

Semantic Search

Semantics refers to the meaning or logic of a language. In semantic search, the algorithm must interpret the meaning of the query. Sometimes we don’t always know the right word or confuse denotations and connotations. Semantic research works to improve search accuracy by understanding your intent and context.

Learn more about semantic search in our previous blog post.

Natural language processing breaks down your query into elemental pieces to understand your query. It understands that when you type “greatful” you mean “grateful,” or other misspellings such as names and will provide you with accurate results. Natural language processing intelligence parses words such as “infographic” and “infographics” to display meaningful results.

Voice search

With voice search, the artificial intelligence must continue to process your natural language and give you the answer in everyday language as well. It very well can’t give you the answer in its language of zeros and ones.

Voice search with Amazon’s cloud-based voice service, Alexa, uses natural language processing to understand context to bring users intuitive experiences. Alexa can search the web, movies, TV episodes and more.

Sooner rather than later, business apps will adopt voice search into mobile apps to move with the trend. Think of sales rep driving to a client meeting. They need to easily search for a PDF and a PPT while waiting at a traffic light. The ideal and quickest scenario is to “talk” to the app to make the request rather than typing it out.

 Productivity

According to Pareto’s 80/20 Rule, 20 percent of inputs or activities are responsible for 80 percent of the outcomes or results. So at the end of the work day, 20 percent of manual tasks (our input) account for 80 percent of recoverable time (our output). You can blame loads of wasted work hours on the inefficiency of searching for documents. It’s a big reason behind the slow-grind progress on those “gotta-do” projects. Natural language processing increases productivity. It decreases time searching and makes problem-solving easy.

Natural language processing should be a criteria for companies considering adding or changing their customer relationship management, marketing automation or other management systems. Natural language processing enhances search in these systems. You can search “Q1 chart showing performance metrics,” and it will present you with accurate and relevant results.

When looking for a management system, natural language search isn’t usually on our criteria or checklists, but whether you’re looking through dozens, hundreds or thousands of files or data, searching in any form should be seamless and done with one entry.

Search can be cumbersome, but with natural language processing, it becomes a personal assistant and helps you find the information you need fast.