¿Qué es el procesamiento del lenguaje natural?

Natural Language Processing (NLP) is a broad field of computer science that involves developing software and algorithms to analyze, understand, and manipulate human language. This can include everything from speech recognition and machine translation to sentiment analysis and text mining.

While Natural Language Understanding (NLU) focuses on understanding the meaning behind spoken or written language, Natural Language Generation (NLG) is concerned with generating language for machines, such as text or audio. Together, these fields comprise the core components of natural language processing and are essential tools in developing intelligent software and systems.

Chances are that your business is constantly collecting data on its customers, including their spending habits, location, demographics, and more. A considerable amount of this data will likely be unstructured. Without the right technology to process the information, it becomes virtually useless. 

That’s where natural language processing (NLP) comes into play.

Developing and deploying sophisticated natural language applications are essential in today’s business environment.  

So, let’s delve into what NLP is, its evolution, and the importance and need of NLP in the business domain, along with various strategies on how your organization can prepare for NLP. 

What do we mean by “Natural Language”?

Natural language refers to the forms of communication that humans use, such as speech and writing. In order for a machine to understand and manipulate human language, it must be able to interpret the text or speech. This is no easy task, as natural language is often ambiguous, imprecise, and full of idioms and slang. 

NLP is a branch of computer science that deals with the interpretation and manipulation of human language by software. Some common examples of natural language processing include speech recognition, machine translation, sentiment analysis, text mining, and word sense disambiguation.

How does Natural Language Processing work?

The ultimate goal of NLP is to enable computers to understand and process human language in order to provide intelligent responses.

This requires the use of machine learning algorithms, statistical methods, and other techniques to analyze vast amounts of data, including text, speech, images, and video.

Some of the key tools used in NLP include:

  • Natural language understanding 
  • Computational linguistics
  • Deep learning models
  • Word sense disambiguation 
  • Machine translation 

To successfully implement NLP in your business, you will need access to advanced machine learning and artificial intelligence platforms that are capable of handling these complex algorithms and techniques.

Some common tools used in NLP include:

  • Machine learning algorithms: These are used to learn the mapping from input data to output labels or predictions.
  • Natural language processing libraries: These provide pre-trained models and common algorithms that can be used for many NLP tasks. Examples include NLTK, spaCy, and Stanford CoreNLP.
  • Annotation tools: These are used to label text with information that can be used by machine learning algorithms. For example, you may use an annotation tool to label a set of texts with their sentiment scores (positive, negative, neutral).

As NLP continues to evolve and become more sophisticated, it is increasingly important for businesses to stay on top of the latest developments in this rapidly changing field. With the right tools and strategies in place, your business can be well-positioned to take advantage of the many benefits that NLP has to offer.

Let’s examine a real-world example of how NLP works, which is a testament to how much the average American relies on an intelligent assistant and the subsequent interaction. Say you are listening to a song on a music station, and you like it. You need to say, “Alexa, I like this song.” The device playing the music will instantaneously lower the volume and reply, “OK. Rating saved,” in what will familiarly sound like a humanlike voice. Its algorithm will pick up this rating and adapt to it such that the next time you are playing music on that device, that song and a selection of those that sound something close to it will play. 

A closer look at this interaction shows that the device activated when it heard you speak, understood the intent of the communication, executed the action, and finally provided feedback in an error-free English sentence. This interaction has been made possible by NLP, along with machine learning and deep learning. 

The power of NLP and the stunts that the tech can pull is definitely attention-grabbing, but are they really indicative of what the tech can do for businesses?

Why is NLP important to your business? 

One of the main reasons why NLP is important to businesses is that it can help you automate a variety of tasks that are currently being done manually.

Your business could leverage NLP in the following scenarios:

  • Customer service – Ensure that you have the ability to respond quickly and helpfully to customer queries. NLP presents the opportunity that is chatbots for customer support. 
  • Boosting your conversion rates – Using NLP solutions powered with tools like chatbots and modern search functionalities should provide you with insights into the overall customer experience for your services and ultimately help you convert potential clients into paying customers. 
  • Sentiment analysis guides – You can gain specific insights into the content and structure of a document, internal systems, and online reviews, among others. You are able to take an undertaking that would have otherwise taken days or even weeks and perform it in just a few minutes or less. 
  • Better understand clients – With NLP, you should be able to analyze the language of your target audience, which translates to a greater ability to identify market segment opportunities. Besides, by leveraging NLP tools, you should be able to identify the customers that might be dissatisfied, the type of products they need, and the specific part that they are in within the sales funnel.    

NLP gives your business a leg up in the marketplace and is likely to do so for years to come. How can your business prepare itself for this future?

How can your business prepare for NLP? 

It all starts with data. Data is the fuel that powers NLP and allows businesses to glean insights that they otherwise would not have been able to obtain. The more data businesses have, the better they will be able to leverage NLP to their advantage. 

Businesses should also focus on building and maintaining clean data sets. This is important because NLP works best when it can rely on high-quality data that is free of errors. A single bad data point can completely throw off an NLP model, which is why businesses need to be vigilant about the quality of their data sets.

One of the ways businesses can prepare for NLP is by investing in data annotation services. Data annotation is the process of labeling data so that it can be used to train machine learning models. This labeling can be done in a variety of ways, including image classification, entity recognition, and sentiment analysis. 

Language-based AI tools are the future 

At present, your business has at its disposal powerful generalizable language-based AI tools, and this is expected only to be the beginning. We should see a rollout of more advanced language-based AI technologies that understand a wide variety of text data assets and various cognitive tasks. At Sigma, we have over 30 years of experience helping organizations like yours develop and apply AI in real-world conditions. Language is our specialty. We cover 300+ languages and dialects, with more added every day.  Contact us today to learn more about how we can help you use NLP to your advantage.

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Sigma offers tailor-made solutions for data teams annotating large volumes of training data.