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Natural Language Processing: Definition and Corporate Benefits

The two most disruptive digital techs by now – Artificial Intelligence (AI) and Machine Learning have already transformed customers’ entire behavioral model when communicating with businesses. Several decades ago people had to look through the magazine or the guidebook to find a worthy restaurant for dinner or a suitable hotel for a couple of nights in a city.

However, these days if anyone wants to get similar information, the only thing he has to do is to make a quick search on the internet by entering the short request and getting a hundred options for a split second. Almost the same principle which is often called Natural Language Processing is emerging in the business apps segment and this is why the software development industry is going to be transformed drastically.

How does Natural Language Processing work?

As you may understand from its name, Natural Language Processing is a figuration of Artificial Intelligence capable of analyzing human language. Although NLP can be represented in various formats, in the essence this technology assists the computer to recognize the human language, to process it, and to give relevant answers.

However, nowadays we are not able to comprehend the entire scope of opportunities that will be provided by NLP in the future since we are in the early stage of Natural Language Processing integration in digital systems. Indeed, the advantages of NLP have already become apparent but there are still lots that have to be explored and practiced.

In words of one syllable, the first stage of NLP adoption hinges on the application of the digital solution. For instance, voice-driven solutions such as Alexa and Google Assistant are interpreting human speech into text that is performed thanks to using the Hidden Markov Models principle (HMM).

Basically, the Hidden Markov Model system deploys mathematical models to understand the meaning of voice messages and to interpret it into the text applicable for Natural Language Processing system. Stripped of fine words, the HMM divides the speech into tiny cuts of size 10-20 milliseconds, searches the phonemes which are the minimum phonetic units, and analyzes them against their own library previously setup.

The further step is the actual comprehension of the meaning of speech. There are various mechanisms deployed by NLP solutions, yet all of them have lots in common: the system defines to which part of speed each word belongs to (for instance, verb, pronoun, and so on).

These mechanisms are normally reinforced with the sets of digitized grammar rules embodied in software algorithms producing machine learning activities focused on the definition of speech context.

In case you are using a system different from speech-to-text Natural Language Processing, it will omit the first stage and will jump immediately to processing the text by utilizing grammar-based algorithms.

Finally, the NLP system has to sort the speed in multiple various manners. By changing the settings of NLP, it is possible to apply the results obtained in different fields. For instance, textual analysis can be used for detecting specific words in the texts related to products merchandised online.

You might be interested: Pattern Recognition in ML

Semantic Parsing

Speaking about Natural Language Processing it is also worth bringing to light a semantic analysis since it is tightly related to NLP and some experts believe that semantic analysis is a mainstay of NLP.

Semantic parsing means the logical patterns of human speech comprehension by Artificial Intelligence of NLP. After the Hidden Markov Model divides the sentences into the smallest meaningful pieces, semantic parsing facilitates context processing.

For example, when Natural Language Processing solutions detect the word “flow”, it has to figure out whether it signifies streaming water, being a more tangible thing, or a sort of process, being more abstractive notion.

Therefore, as long as the HMM approach divides the text and NLP enables the transfer of text content from a human to a computer, semantic analysis finds the meaning of provided information deriving all possible contexts.

If we used NLP without semantic analysis, we would get a limited range of text comprehension options, so we wouldn’t reach the current level of textual communication between a human and a computer. Yet, deployment of semantic parsing is boosting Natural Language Processing tech and the quality of human speech recognition is going to improve in the future.

What’s in store for corporate NLP

Since the end of the previous century, almost for 30 years, Enterprise Resource Planning software has pulled off the lead on the market of corporate software products. The cause these software manufacturers grew so strong is that they elaborate complex working cycles that the customers have to work out to the last detail and pass stepwise to attain routine objectives.

Usually, it takes much time – months or even years, to get the hand of these systems deployment by thoroughly practicing and this is where the businesses and the experts reach the obvious conclusion that this qualified expertise is put on the same basis as obtaining comprehensive educational level. This emergence provided the world-known giants such as IBM, Oracle, SAP, and others with the possibility to generate large-scale user communities that feed a need in their solutions in various segments.

Nevertheless, the possibilities granted by Natural Language Processing allow users to cut off a corner when passing through these menu-oriented circles to reach the information they are interested in, therefore, eliminating the necessity for hard exercising and custom configurations fueled by the developers of Enterprise Resource Planning software. This tendency is expected to mainstream in the future, thus reducing incomes of ERP service companies and related businesses which have already experienced recession because of apps’ transfer to cloud platforms during the last ten or fifteen years.

However, the more important thing is how Natural Language Processing will affect the pursued commitment of the aforementioned user communities to ERP products. Indeed, a hotel manager who employs browser search to find the closest car rental service upon a client’s request is also likely to use NLP searching algorithms in the internal human resource management system to look for a new room maid who speaks Portuguese. Naturally, he will avoid using various menus and training to find the same information. Therefore, since more easy-to-use Natural Language Processing cutoffs will mitigate the necessity for extensive training, ERP qualifications will become less popular.

Onward boosting this trend means that the new generation leaders will come to the fore. The oldest representative of Millennials is about 35 years in 2018. Since Millennials have grown up with mobile internet in their hands, they expect to search and getting information swiftly and conveniently. If they have to pass through hard exercising and are unable to get quick replies, they are likely to choose another platform. Allowing for the commercial benefits of faster access to data, that cuts expenditures and ensures advanced deeper insights for far-reaching forecasting, you’ll get a top-notch business model.

Being an efficient technology Natural Language Processing is continuously involved in self-improvement by adopting Machine Learning techniques. When Artificial Intelligence is deployed, it is supplied with tons of notions and structures, so the system is capable of selecting the most optimal information to respond to users’ search requests. For instance, when the user submits a search request through Google, the system is expected to respond with the list of results ranked according to relevance degree. However, Google goes the extra mile and it learns those picks made by the users from that list to collect the most probable options which facilitate each next search request processing. Moreover, the system notices and memorizes related details such as linked terms and patterns that are frequently repeated in the requests and the results to generate a more relevant response in the future.

The experts assume that it will be rather easy for Artificial Intelligence to progress in the business environment. Such simplicity can be explained by the fact that the glossary of notions and patterns related to commercial topics such as human resources management and financial operations are smaller than glossaries deployed by search systems daily when responding to the requests of regular users.

For instance, the human resources glossary used in the software ecosystem amounts to less than one thousand terms which might seem a solid stock of data to be extracted when selecting suitable information but, in fact, it is a rather limited number comparing to other search fields.

Natural Language Processing Examples

There are many examples of natural language processing technology that we are used to and take for granted by now. Here are a few to illustrate the point.

Email filters are the most basic examples of NLP that assess the contents of the incoming email and decide whether this email is okay or should be considered malicious and therefore sent to a spam folder or blocked altogether. 

Search results are also getting smarter and smarter. When you enter something in Google, its algorithms understand what you are typing and either suggest you the available options (think of the Knowsmore from Ralph Breaks the Internet) or says "Perhaps this is what you meant" in case you had a typo. This simplifies and optimizes the search process. 

Language translation tools are improving the quality of the translation thanks to the NLP because the algorithms don't simply translate the words as they are, but also look for the context of the overall message. 


Although this transformation may take some time, the ascendence of Natural Language Processing is near at hand. The synthesis of innovative potential, cutting-edge digital techniques, reinforced functionality, and discovery of new leadership opportunities will establish a new business environment where the businesses have to accommodate by following this shift. Indeed, tech giants will reshape themselves to match new conditions, but at the same time, it will open a huge window of opportunities for rising vendors to sit pretty on the market and to change the development horizons in their favor.

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