Natural Language Processing: all about Natural Language Processing

Natural Language Processing (NLP) is a technology that allows machines to understand human language through artificial intelligence. Find out everything you need to know about it.

With the predicted rise of artificial intelligence, we will soon be brought into daily contact with robots and other digital entities. However.., in order to be able to cooperate or even cohabit with machines, it is necessary to be able to communicate with them. This is what Natural Language Processing technology is all about.

What is natural language processing?

Natural Language Processing (NLP) is an artificial intelligence technology that aims to enable computers to understand human language.

The aim of this technology is to enable machines to read, decipher, understand and make sense of human language. D’significant progress has been made in this area in recent yearsand natural language processing is now used for a wide variety of use cases…

What are the usual cases of use?

You may not know it, but many applications that you use every day are based on natural language processing. Examples include translation applications such as Google Translate or PDAs such as Apple Siri, Microsoft Cortana, Amazon Alexa or Microsoft Cortana. The same applies to all chatbots.

Similarly, the word processors such as Microsoft Word and Grammarly use NLP to check grammar and spelling. Finally, Interactive Voice Response (IVR) type applications used by call centres allow certain requests to be processed automatically.

These are just a few concrete examples used by ordinary people. However, there are a myriad of applications for this technology. In general termsall programs based on machine understanding of language… are based on the NLP.

How does natural language processing work?

Most natural language processing techniques are based on Deep Learning. The Artificial intelligence algorithms are trained from data to learn to analyze human language for patterns and correlations.

The role of algorithms is to identify and extract rules from natural language in order to convert unstructured language data into a form that computers will be able to understand.

In the past, old approaches to natural language processing were based on a rules-based approach. The Machine Learning algorithms of the time were instructed to look for words and phrases in a text and give specific answers based on them. However, Deep Learning allows for a more flexible, intuitive approach that is closer to natural language and how humans learn it in childhood.

As a general rule, an interaction between humans and machines via the NLP is based on the following First, the human speaks to the machine. The machine captures the sound, and converts it into text. The text data is processed and then converted back into audio data. The machine executes the audio file to respond to the human interlocutor.

What are the different techniques of NLP?

The two main techniques used for natural language processing are syntactic and semantic analysis. Syntactic analysis consists in identifying the grammatical rules in a sentence in order to decipher its meaning.

Several semantic analysis techniques exist. Parsing” consists in analyzing the grammar of a sentence. Word segmentation consists of dividing a text into units, while morphological segmentation divides words into groups.

The semantic analysis consists in directly decipher the meaning of a text using algorithms to analyze words and sentence structure. Algorithms may be context-based, for example, or they may compare texts with databases to understand their meaning. However, this is a complex approach and no algorithm that is really capable of understanding the meaning of a text in this way exists at the moment .

What are the difficulties related to natural language processing?

Natural language processing is far from being an easy task. For good reason, human language is by nature complex and its different rules are difficult for a computer to understand.

Some of these rules can be very abstract. For example, when a person uses a sarcastic remark to convey a subtle message. It is almost impossible for a modern machine to perceive such nuances….

Similarly, the use of the letter “s” to signify plurality is complex for a machine to assimilate. To truly understand human language, it is need to understand both the words and how the concepts are connected to deliver a message.

If humans can easily master a language, ambiguity and imprecise characteristics of language make the task much more complicated for machines that are not used to “thinking” in this way. Humans usually “talk” to computers using precise, concrete and highly structured programming languages.

However, investments and research in the field of Natural Language Processing are increasingly important. Over the next few years, this technology will develop at a rapid pace and machines will very soon be able to communicate with humans in a completely natural way…

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