what is Natural Language Processing and how does it work?

what is Natural Language Processing and how does it work?


the Natural Language Processing (NLP) is a branch of artificial intelligence, which aims to make the use of technologies simpler and more pleasant. To do this, it is integrated into computer programs to enable them to understand human language. Thus, it is used daily through many tools. Automatic translators, search engines, voice interfaces, it’s everywhere.

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What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) stands for “Automatic Natural Language Processing” (TALN) in French. These are computer programs developed for the purpose of understanding language as it is written or spoken by humans. The algorithms involved in NLP are able to analyze the meaning of words. This technology powers both automatic translators and virtual assistants.

NLP is a branch of artificial intelligence (AI). The use of artificial neural networks is advancing research in this field by leaps and bounds. Indeed, this process of understanding human language combines linguistic, computer, mathematical and deep learning knowledge.

To work, the computer language needs a marked-up, precise and unequivocal programming language. The so-called “natural” language is much more complex through its innuendoes, its humorous traits or even its metaphors. Therefore, NLP and AI learn to structure and interpret the different human languages ​​to generate computer language.

How did Natural Language Processing develop?

In the 1960s and 1970s, the first experiments in Natural Language Processing concerned machine translation. Today’s online translators and spell checkers are successful examples of this early research.

Alongside machine translation research, the first conversational robot in history was born in the United States: ELIZA was created by Joseph Weizenbaum in 1964.

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In the 1980s, the increase in computer processing capacities gave new impetus to NLP. This progress is accompanied by the introduction of machine learning algorithms. Machines then become able to create their own rules through learning from texts.

It was in the 1990s that the first system based on artificial neural networks was born. This breakthrough allowed the development of the first bank check reading system.

In the history of NLP development, the more recent advances are also the most impressive. Automatic language processing has been around for a long time, but the major digital industries have developed cutting-edge tools.

Among the main models of NLP, there are:

  • Google’s BERT algorithm.
  • ALBERT from Google, which uses 89% fewer parameters than the BERT model.
  • The RoBERTa program, a derivative model created by Facebook.
  • The DeBERTa program, which is also a derivative model developed by Microsoft.
  • Microsoft’s UniLM, an alternative model.
  • Reformer from Google, also an alternative program.

Interest and examples of use of Natural Language Processing

What is the point of NLP?

The point of computer language processing is to help humans and machines speak the same language. Computers analyze language to convert it into raw data. The information thus produced makes it possible to generate interactions with users to create intelligent conversations.

The NLP is still booming since all the conditions are met to promote its development:

  • Continuous improvement of deep learning.
  • Exponential computing power of computers.
  • Massive increase in available royalty-free data.

3 concrete examples of using NLP

1 – Voice assistants

These are interfaces between users and content (or service) providers. Connected speakers are one of those personal assistants that make everyday life easier.

2 – Conversational agents or chatbots

These computer programs simulate a human conversation. They allow interaction to obtain answers to questions asked by a user.

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3 – Machine translation

These instant services translate content written in various languages. Most often, the texts are different depending on the translator used. This is due to the fact that machine translation requires a lot of knowledge, which concerns both the source language and the target language (syntax, semantics).

How does Natural Language Processing work?

This discipline of AI is becoming increasingly adept at understanding and speaking human natural language. To succeed in this tour de force, several levels of language processing are necessary. The methods differ from one program to another; however, the prerequisite steps often remain the same.

Natural language processing techniques use:

  • Lexical analysis.
  • Syntactic analysis.
  • Semantic analysis.
  • pragmatic analysis.

Lexical analysis

Lexical analysis is often the entry point for many data compilations in NLP. It consists of extracting words and parts of text to try to understand their meaning more precisely. Lexical analysis can take many forms.

It interprets context, an ability that humans naturally have, but computers don’t. She also tries to understand the relationships that are established between words.

Words and groups of words are labeled according to their grammatical categories. This classification into different groups (articles, verbs, nouns, etc.) is called segmentation into lexical units or “tokenization”.

It is also possible to interpret the words according to their lexical root according to a “rooting” process or even to take the canonical form of the words to classify them. This lexical processing is called “lemmatization”.

Syntactic analysis

Syntactic analysis, also known by its English name “parsing”, studies the structure of sentences. The objective is to understand the existing relationships between words by taking into account both the vocabulary and the rules of syntax. These relations are studied and organized within syntactic trees. This analysis allowed, among other things, the creation of grammatical correctors.

Semantic analysis

Semantic analysis focuses on the meaning of words and sentences. This is a complex task due to the natural ambiguity of human language.

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Indeed, a word can have several meanings and the same goes for a sentence. Here are two concrete examples:

  • The word “mouse” refers either to the animal or to the computer tool.
  • The word “strawberry” refers either to the fruit, or to the dentist’s tool, or to the item of costume that is worn around the neck.

Semantic analysis looks for the relationships between words according to the different possible concepts and representations. It is supported in this quest for meaning by pragmatic analysis.

pragmatic analysis

Pragmatic analysis analyzes words and phrases that are close to each other. This step allows an anchoring of the meaning by corroborating the semantic results previously obtained.

Broader elements are taken into consideration such as the reference universe of the enunciator and the universe of knowledge of the human group to which he belongs.

The role of pragmatic analysis is also to deduce and interpret what is not explicitly said. For example, if a person says, “I don’t know what to get my family this year,” it is implied that they are talking about the holiday season.

What is the future of NLP?

Finally, even if the progress of NLP is considerable, the mysteries of so-called “natural” language are still far from being solved. This does not deprive this technology of its great utility in everyday life for pragmatic reasons. However, it will still be necessary to wait a little before being able to have a conversation as elaborate with a computer as with a human being.

However, the use of artificial neural networks that allow machines to learn by themselves opens up many possibilities, including the possibility that machines will one day be able to understand human beings perfectly.

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