NLP (Natural Language Processing): kézako?

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Unlike humans, computers only understand the language of code, which hinders interaction. By equipping these machines with the ability to understand human language, interaction becomes more intuitive. This is what NLP is for.

Through this discipline, companies can develop advanced algorithms to:

  • provide customer service,
  • find relevant information,
  • assist their customers (Cortana, Siri),
  • analyze their reputation (text mining), etc.

This article aims to give you an overview of NLP and its uses.

What is NLP?

NLP, Natural Language Processing or Automatic Natural Language Processing in French (TALN) is a discipline of artificial intelligence whose goal is to give machines the ability to understand and generate human language (written or spoken).

💡It acts as an interface between linguistics and computer science.

Concretely, NLP is based on the understanding, manipulation and generation of natural language by machines with a view to promoting machine/human interaction.

It usually comes in two main parts:

  1. the NLU, Natural Language Understanding or Natural Language Understanding. This part brings together machine learning models aimed at in-depth understanding of data and exchanges. Its role is to identify the intentions behind the writings and words of humans.
  2. the NLG, Natural Language Generation or Natural Language Generation. It brings together machine learning language models whose purpose is to create and automatically generate texts like a human.
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How does NLP work?

The goal of NLP is to make sense of linguistic data from humans so that it can be understood by a computer.. To do this, machines use sensors similar to our eyes and ears to read and listen.

The understanding of the natural language is then done thanks to a semantic analysis or a syntactic analysis provided by computer programs. NLP projects are essentially divided into two aspects:

  1. The language component (data preprocessing). The collected information is transformed into inputs or a dataset.
  2. The Data Science component or machine learning (algorithm development). Deep Learning or Machine Learning models are applied to the dataset.

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1 The data pre-processing phase

This step consists of cleaning the collected data (deleting emoji, deleting urls, etc.) to make them usable by the machine.

For this, we use several NLP approaches based on programming languages ​​such as python and R. Among the methods used, we have:

  • Word bags to count the words of a text.
  • Tokenization to segment text into sentences or words.
  • Stemming to remove prefixes and suffixes.
  • Lemmatization to reduce a word to its basic form.
  • The removal of Stop Words (removal of stop words).

We also transform textual data into digital data before applying Machine Learning methods to them.

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This is done in particular through different approaches such as:

  • the Term-Frequency (TF),
  • and the Term Frequency-Inverse Document Frequency (TF-IDF).

2 The learning phase

This step consists of developing the data interpretation algorithm. The three most widely used Natural Language Processing approaches are:

1 Rule-Based Methods

These methods are mainly based on the development of linguistic rules specific to a domain.

These can be used to solve relatively simple problems, such as extracting structured data from unstructured data (for example, classifying unwanted emails as spam).

2 Methods based on machine learning

Classic machine learning methods applied to NLP are used to solve more complex problems.

They are more focused on language comprehension; machine learning algorithms exploit pre-processed data. Furthermore, thanks to their machine learning capability, the algorithms can also use data relating to the occurrence of specific words, the length of sentences, etc. They usually use static methods.

3 Methods based on deep learning

The use of deep learning models for NLP (deep learning language processing) projects uses neural networks.

These perform automatic feature extraction, which does not require complex pre-processing. Thanks to their power, deep learning algorithms manage to perform even more difficult LNP tasks, such as translation.

Some Uses of NLP

As an AI-based technology, NLP algorithms are useful in many tasks:

Website SEO

Google recently implemented an NLP algorithm for its BERT search engine to better understand the deep meaning of user queries, not limited to keywords.

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Machine translation

Applications such as Google Translator use machine translation algorithms developed with NLP techniques to translate entire texts without any human intervention.

These include Statistical Machine Translation.

Online trend analysis

Commercial companies use NLP algorithms to identify customer reviews of a product or service.

This is the sentiment analysis technique. It is also used to make strategic marketing and business decisions based on customer preferences.

Marketing

Marketers use NLP to find potential customers. Google uses it in particular to generate profit through its advertisements.

What is the origin of the LNP?

Natural Language Processing (NLP) begins with the beginnings of computing in the 1940s and 1950s.

The research fields then focused on the translation of simple sentences. Thanks to advances in artificial intelligence (AI), NLP has advanced to make our lives easier.

What is NLP used for?

Natural Language Processing uses neural networks to automate the execution of different tasks such as:

  • the recognition of named entities (places, names of people, etc.),
  • aspect extraction,
  • the production of automatic summaries,
  • text recognition and classification, etc.

What are the benefits of NLP?

AI-guided NLP tools make it easy to perform complex tasks such as searching for precise information, translating, text contraction, etc.

For companies, NLP has many other advantages:

  • use of personal assistants,
  • achieving better targeting for marketing campaigns,
  • better management of customer reviews,
  • automated customer service available 24 hours a day, etc.

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