Natural language processing (NLP) gives software the ability to understand human language as it is commonly spoken. A distinguishing factor of NLP is that it has an element of artificial intelligence (AI). Now is the time to start thinking about questions to ask (TechGenies) and brainstorming ideas for enterprise software implementation of this amazing new feature available for business software.
Although the development of NLP applications has been challenging over the past few decades, we are finally making great strides in this realm of technological advancement. The reason it has been such a struggle to implement intuitive search capabilities is because computers have conventionally required humans to engage with them in a specific programming dialect. These computing languages cannot be ambiguous with broken sentence structure like our own human patterns of speech. Some of the most well known uses we have seen in this area so far are Google search, Siri, and Alexa which require clearly spoken voice commands with specific search terms trigging results. Because human language is not always clear or unambiguous, the advances and practical uses of NLP have been stifled in business software … until now!
Some Uses of NLP for Enterprise Software
Natural language processing is most commonly used in search functions. This includes allowing users to request data sets by using questions and commonly spoken language as search terms. Instead of asking a human in the HR department a people analytics question like, “Hey Sam, can you get me a turnover report sorted by performance score and department?” Now we can let the Sams of the world focus on keeping their own performance scores high by utilizing the NLP feature built into business software, like TrenData’s People Analytics Solution for example.
Another sector, outside of HR that is reaping the benefits of NLP is medical facilities. Think about the hundreds, thousand, even millions of medical records any given physician’s practice, medical NGO initiative, or hospital software program must go through to find the people, specific symptoms, diagnoses, or other vital data in order to utilize or analyze that data in a significant way.
Historically, specialists have had to do this work because the search queries needed to be very specific and were innately complex to say the least, but that’s not the case anymore thanks to NLP.
We are living in a time when social media posts and internet based company or product comments and reviews can almost literally make or break a business. Some companies like, Sprout Social, are using NLP to help businesses monitor brand performance across the digital social landscape. Now with natural language search terms, in conjunction with some other common tools, businesses can see who is talking about a company online, and it’s easier than ever before to engage with these audiences who mean the most to a brand.
So How Does NLP Work?
Through forms of AI, like neural networks and deep learning models, NLP can extract, examine and utilize patterns in stored data, and the value of results provided to the user to improve itself with each new search. Have you ever Googled something and gotten the response, “did you mean ___ ?” That’s NLP schooling itself to learn users’ intent despite mistakes or common language styles that baffle most software’s search algorithms.
Before machine learning came along, information in text documents, HR files, patient records, or any number of text files buried in a system were not able to be analyzed. So much great data was being lost and wasted, but NLP has provided a way to make squandered data a thing of the past and given way to practical and useful advancements businesses!
Essentially, such easy access to this information is provided by the use of algorithms to:
- Read text written in our human languages
- Interpret its meaning through its own complex filters, and
- Translate it back to us providing exactly what we asked for
Thanks to big data and cloud computing, NLP has taken some major leaps forward. What used to be insurmountable mountains of data, is now a casual hike for machine learning. NLP has now gained the ability to understand our ambiguous human languages making natural language search through troves of data a walk in the park.
This is done in part by providing more weight to certain words, phrases, or even slang over others terms which we may include in our searches that are not really relevant to the results we want from our query. This is part of the reason why the more we interact with NLP, the more the system grows, develops and unfolds into a tool that provides more valuable results consistently as time progresses.
NLP is a powerful feature providing a multitude of tangible benefits that we (TechGenies) can now integrate or build into business software.
As an instance of NLP within a software program gets smarter, so to speak, its flexible and intuitive nature is expressed by providing results that don’t just give you what ask for, but rather what you intended to ask for.
I think we can agree that getting exactly what you want is a good thing.
Remember, “Did you mean ___?”
Yes, Google. That’s what we meant. Thank you.
Originally posted by Jonathan Webster for TechGenies