NLP: It’s all About Natural Language Understanding by Machines

 Do you think of Artificial Intelligence as a terminator that will replace us at work?

It is true that machines will automate more and more repetitive tasks, but they will not be able to replace us in cognitive ones or in jobs that require human skills.

Terms like Big Data and Artificial Intelligence are not new to our ears. Neither are some of their advantages and contributions to society in the field of transport, security, or even health.

However, the debate around the ethics that should guide machines and the confusion, regarding the image we have of them is also evident.

Big Data, Internet of Things (IoT), and Artificial Intelligence, among other cutting-edge technologies, were the topics of the eighth edition of Big Things and people expect more beyond these robust technologies. But with the expanding advancements there enters the names of Natural Language Processing (NLP).

Natural Language Processing (NLP)

The Natural Language Processing (NLP) is the practice of understanding how people organize their thoughts, feelings, language, and behavior, with the aim of getting played on the machines, an interpretation of similar language to that of beings humans.

It is a discipline that extends to Computing, Artificial Intelligence, and linguistics. After computer vision based on machine learning, it is the technique that has made the most progress in its applications.

Along with speech recognition it has seen growth in recent years. In the case of NLP, investigations into it have increased by 55%

Components of Natural Language Processing

The analysis described below applies to any NLP task, it depends on the purpose of the application.

• Morphological or lexical analysis

It consists of the internal analysis of the words that form sentences to extract slogans, inflectional features, compound lexical units. It is essential for the basic information: syntactic category and lexical meaning.

• Syntactic analysis

It consists of analyzing the structure of sentences according to the grammatical model used.

• Semantic analysis

It provides sentence interpretation, once morphosyntactic ambiguities have been eliminated.

• Pragmatic analysis


Incorporate the analysis of the context of use into the final interpretation. This includes the treatment of figurative language as the knowledge of the specific world necessary to understand a specialized text.

A morphological, syntactic, semantic, semantic, or pragmatic analysis will be applied depending on the objective of the application. For example, a text-to-speech converter does not need semantic or pragmatic analysis.

But a conversational system requires very detailed information on the context and the subject domain.

Applications of Natural Language Processing

-On one hand, it helps to filter and discover insights in the enormous whirlpool of data. The data has grown with leaps and bounds from the past two decades and the trend is unstoppable.

It is predicted that in 2025 more than 175ZB(zettabyte) will have been created in the world, a record 5 times higher than in 2018.

-Second, it allows the promotion of great innovations of practical application, such as voice search by Google, the personal assistant of Alexa, or the recommendation system of Spotify.

The latter, by the way, uses along with the NLP algorithms based on usage practices and recognizes musical patterns in songs of its recommendations.

-The NLP also makes the data industry and Business Intelligence more friendly for less technical profiles. This is where it translates Big Data into concepts that are more understandable to humans. It, therefore, makes it closer to Small Data.

-24/7 services are increasingly difficult to cover by human personnel. That is where chatbots come in, responsible for the conversations we have with web pages, and which try to emulate human ones.

Also, the sentiment analysis to extract content from the comments of social networks to respond to them. Finally, all the practices made by robots prepared for interaction with humans more friendly.

-Digital marketing has also been driven by NLP by making more effective interactions in the customer’s journey. If the client feels that the brand “speaks” to him, if there is not always a human being behind, he feels accompanied throughout the process.

We are moving towards a more effective omnichannel ecosystem. In it, the conversation with the brand is never interrupted, regardless of the environment in which the customer moves.

In short, the applications of Natural Language Processing are multiple and are no longer a thing of the future, but of our most everyday reality.

[Prefer Reading: “How Natural Language Processing (NLP) Aids Sentiment Analysis?” ]

Is NLP Perfect?

The concept of natural language processing involves high tech computing which involves a range of limitations posing towards its capabilities.

Users have high expectations from every new technology launched but we cannot be far away from the unexpected experiences or mistakes Siri or Alexa make.

Certainly today, experts have been able to forge machines that are powered with complex algorithms that are able to make business decisions yet, no decision can be as precise and wise as that of a human being.

With NLP, the search capabilities can be enhanced where the act of searching and sorting can be made easier, however; the results incurred may be flawed that could further impact the expected results.

From a user’s point of view, they simply want to access the device and are least bothered about where the data resides, how the output is made based on the input, the biggest possibilities of data accuracy, etc.

“The interface is still relying on the user to ask the right question. There’s no value judgment from these systems. It’s simply you ask me a question and I’ll give you the answer,” said Steven Mills, associate director of machine learning and AI at Boston Consulting Group’s Federal Division (BCG Fed).

Further, he stated that “The power of the data science field is not simply the technical expertise, but an understanding of how to do analysis, how to interrogate the data to get the kinds of business insights we really need. We tend to forget about that second piece of the equation.”

What the Future Holds On To?

A lot more is yet to come where we need to understand that natural language understanding involves text along with context which binds the relationship between words and text for it to deliver accurate expected outcomes.

BI (Business Intelligence) and analytics will erupt new offerings with NLP capabilities to stay competitive and innovative. Thinking about the next big thing: an exploration of NLP?

It will be venturing into voice interfaces which will be highly imperative, interactive, and innovative to be able to handle data in a more structured and organized way.