From customer behavior to daily or seasonal trends, the typical data warehouse has diverse amalgamation of data. Businesses have started using the insights gained from this information to drive a new domain of customer understanding. Unlike past, majority of data created today is unstructured because of which organisations are turning to Natural Language Processing (NLP) technology to derive understanding from the countless unstructured data available online and in call logs. Following are 4 business applications of Natural Language Processing emerging today that we think will be of great use in coming days:

1. Neural machine translation

Machine translation (MT) used to be absurd, but it has traveled a long path and it’s pretty good now. One can think of early MT as a toddler since natural language processing software learns language in the way a person does. Over time, more words get added to an engine, and soon there’s a teenager who would communicate in an eloquent manner. Machine translation quality is inherently dependent on the number of words you give it and since it takes time, this became one of the main reasons which originally made MT hard to scale.

For businesses whose aspirations don’t allow them to wait for an engine to “grow up,” there’s neural machine translation. With neural machine translation, it is easy for engineers to now cross-apply data. This fundamentally changed the way language processing was done in the past as it speeds up development, taking a machine translation engine to an astounding level of performance in months Vs years. As a result, businesses can safely use MT to translate low-impact content such as product reviews, regulatory docs that no one reads.

2. Chatbots

Chatbots are the newest  natural language processing methods. Initially, chatbots were consumer-facing and many companies were successfully using chatbots to streamline some of the work that would traditionally fall to customer support representatives. They’re trained using text data from past conversations between your customer support representatives and customers. This training allowed bot to identify the meaning of your customers’ requests and queries using context clues and provide an answer that a human will understand. While touch points like these can help drive B2C sales, in a B2B world startups have applied tech to other areas: Most enterprise bots optimize HR. Now, we have natural language processing tools that answer common employee questions:

  • How many leaves does an employee have left?
  • What snacks they want in the breakroom? (by doing a poll among employees)
  • How often employees compliment one another? (by monitoring words like “kudos,” “cheers,” and “props” in usual staff conversations. This helps managers improve retention and morale)
3. Hiring Tools

The flood of resumes can be taxing and time-consuming and the task of sorting through the pile can be downright overwhelming for small-business owners and even for large organizations with robust human resources departments. On the topic of HR, natural language processing software has long helped hiring managers sort through resumes. Using the same techniques as Google search, automated candidate sourcing tools scan applicant CVs to speck people with the desired background for a job. But — like early machine translation — the sorting algorithms these platforms used were not flawless. Say an applicant called herself a “growth hacker” instead of an “sales rep”: Her resume wouldn’t show in results and chances are pretty high that a creative, client-driven candidate might get overlooked.

Today’s systems move afar exact keyword match. They first search for HR’s originally provided keywords and then address the synonym issue by using results to identify new words to look for. Extrapolating new terms (like “growth hacker”) keeps qualified candidates from slipping between the cracks.

Augmented writing tool uses semantic categorization, a natural language processing technique, to help hiring managers craft gender-neutral job descriptions. Some tools provide vocabulary, syntax, and formatting tips like “add more bullet points.” One can easily see a radical improvement in applicant numbers by implementing these changes.

4. Conversational Search

Traditional search has us conditioned to speak like cavemen: choppy sentences, verbs optional. But that’s all starting to change with the rise of digital assistants and conversational search.

Conversational search comes with the expectation that digital assistants will actually converse with us. Our primitive two-word queries (grunts and hand gestures included) have evolved into full-fledged, honest-to-goodness questions. Unlike bot, it’s a voice-activated platform that listens in on company meetings for trigger phrases like “what are” and “I wonder.” When it hears them its search function whirs into action, seeking an answer for the rest of your sentence.

Say, for example, you’re in a board meeting and someone says, “What was the ROI on that last year?” Silently, these conversational bots would scan company financials — or whatever else they asked about — then display results on a screen in the room. Since searching quality information takes almost 30% of an employee’s time, streamlining search in real-time conversation surely promises to improve productivity.

I agree to have my personal information transfered to MailChimp ( more information )
Join over 3.000 like minded AI enthusiasts who are receiving our weekly newsletters talking about the latest development in AI, Machine Learning and other Automation Technologies
We hate spam. Your email address will not be sold or shared with anyone else.

Leave a Reply