Voice assistants Alexa, Google Home and Siri are all based on automatic language processing technologies. Objective: to have the ability to understand, process and generate voice messages.

What is Natural Language Processing (NLP)?

Natural language processing (NLP), or automatic language processing (TALN), is a branch of artificial intelligence that focuses on giving machines the ability to understand, generate or translate human language as it is written. and/or spoken. Chatbots are among the most popular NLP software. Other famous NLP applications: voice assistants Alexa, Google Home or even Siri.

How Does NLP Work?

NLP combines artificial intelligence and linguistic processing. The latest generation of NLP technologies is based on artificial neural networks or simple statistical machine learning models. Learning models will have been trained on large volumes of text.

The objective can target several types of automatic processing: speech-to-text and text-to-speech, recognition of named entities (names of people, places, etc.), sentiment analysis (positive, negative, neutral), text synthesis, aspect extraction (targeting text intent) or topic modeling.

What are the two types of machine learning models in NLP?

Broadly, natural language processing comes in two broad categories of machine learning models:

• Machine learning models oriented NLU (natural language understanding) which seek to grasp the meaning of a language and a discourse in its context,
• Machine learning models oriented NLG (natural language generation) which aim to generate a text like a human.

7 Main Techniques of NLP

1. Text Translation

It is one of the best known and oldest applications of NLP. It is also probably the most used. Traditionally, text translation models only translated sentences word for word. I still remember my English teachers in college, who forbade the use of these systems for their unreliability.

In 5-10 years things have changed a lot. The arrival of translation systems like that of DeepL, the improvement of Google translation, have made these tools much more robust. The increase in the amount of data available and the design of neural network architectures such as LSTMs have helped a lot.

LSTMs have changed the approach used to solve the problem by introducing elements of context. The analysis of the content to be translated is no longer done word by word but by packets of words.

2. Chatbots

Chatbots have become in a few years, an essential tool in many areas. In customer relations, they improve support and free up time for agents. In education, they allow you to quickly get answers to your questions. In the administration, they allow a lower waiting time and help to deal with repetitive and easy to understand requests for an AI.

More generally, chatbots are becoming basic tools in human-computer interactions. Even if from a technical point of view the solutions are more and more robust and reliable, the prospects related to the use of chatbots are limited in many areas. In an article written in 2020, Nobody likes talking to an AI, I explained that areas like customer relations or administration, were a question of human relations and emotions, things that chatbots are always not. unable to copy.

In addition, human interactions are done more naturally by voice, it would not surprise me if in a few years we will do all our research, our purchases and our scientific procedures by voice. In contact with AIs that recognize voices, understand language and are able to detect the emotions of the interlocutor and adapt their voices.

3. Generate Texts

Text translation and smart assistants, while impressive to begin with, are far from new applications. Text generation, on the other hand, is a very high level application, which gives machines a real skill that can help a lot.

The best models of today allow to generate very realistic, complete and coherent texts. Some students on Twitter explain that they use models like GPT-3 to generate the essays requested by their teachers, and it works! Much of the content shared on social media is generated by artificial intelligence models.

4. Text2image

Text2image is a technique that straddles NLP and image processing.

In 2021, OpenAI launched the race for artificial intelligence models that make it possible to generate images, with the DALL-E project. Version 2, DALL-E 2, has meanwhile been released.

5. Content Summarizing

The generation of content assisted by artificial intelligence has offered many perspectives. As models are able to accurately understand what is being said in the text, they know how to filter the content between what is essential, what is important and what can be deleted. This makes them very useful for summarizing long texts.

One could imagine artificial intelligence models capable of transcribing the exchanges during a meeting, and providing a concise summary that contains the necessary information at the end of the meeting.

6. Classification of Texts

Another less impressive but widely used NLP technique is text classification. The first time I did NLP was for text classification. I had to automatically classify product descriptions for an e-commerce site.

Text classification is widely used to cluster films using their synopses, books or posts on social networks.

7. Text Enhancing

When I have to write an important email, or to correct my articles, I often use Grammarly, it is a tool that works with artificial intelligence, and which allows me to improve the text. In addition to offering spelling correction, the model is able to suggest word modifications, putting synonyms or completely changing the turn of the sentence.

Besides, we can give our objective with the text to the template to improve the suggestions. For example, we can write an email for a friend, and send a similar email with the same information to a client, just by asking the AI to change the tone of the email.

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