Human language is quite different from machine language. NLP is a branch of artificial intelligence that analyzes the meaning of our language and integrates it into computer programs using complex algorithms. It is a real interface between computer science and linguistics.
I/Origin of the NLP 🔍
The first NLP experiments were carried out in the aftermath of the Second World War. At that time, it was a symbolic approach, which means that artificial intelligence was based on data and very strict rules that were imposed on it in order to perform one or more tasks in a specific expert domain (a process called "rules based"). The goal was not to have a human-like intelligence that would be able to adapt, but rather to create expert systems.
The first applications were mainly based on translation.
Then, it is in the 80s that NLP will really emerge and improve even more, thanks to machine learning algorithms. The machines then became more autonomous and were able to create rules automatically, based on learning from the data they were given. We are talking about a new approach, called "statistical".
Finally, the important step that marked NLP in the 2000s is the one of "deep learning" with the implementation of artificial neural networks. The difference between deep learning and machine learning is the extraction of automatic features.
*The artificial neural network is a computational model whose design is very schematically inspired by the functioning of biological neurons. In modeling biological circuits, they allow to test some functional hypotheses from neurophysiology, or the consequences of these hypotheses to compare them to the real" definition from techno-science.net.
Today, with the continuous improvement of machine learning algorithms, NLP is booming.
II/The different methods used in
In NLP, there are various methods. However, the main ones used remain :
Syntactic analysis consists of identifying the grammatical rules in a sentence in order to decipher its meaning. It is based on the vocabulary used and on the syntactic rules to understand the relationships between words.
Semantic analysis focuses on the meaning of the text and the messages conveyed. It is based on complex algorithms to analyze words, feelings, sentence structure or to compare data between them.
Lexical analysis is a branch of semantic analysis. It allows to extract the elements of the text (words or groups of words) in order to relate them, to understand their meaning, and to classify them according to their grammatical category.
Propositional analysis, also called propositional semantics, aims to reveal the meaning of the whole sentence. This analysis is based on the meaning of each word and its relationship with its surroundings.
This completes the semantic analysis by providing a context. The enunciation situation and the interlocutor's universe are then taken into account. It is therefore necessary to make deductions or interpret what has been said, which is not always easy for artificial intelligence.
III/Recent advances in NLP 🚀
Analysis of feelings
Also called "opinion mining", sentiment analysis extracts subjective information from a text or an exchange in order to understand the opinion of the interlocutor. The arguments or answers given will then be judged as positive, negative or neutral.
This tool is recent, rapidly expanding and becoming very popular with companies in the social media, marketing and advertising sectors.
Sentiment analysis provides valuable information to help make decisions and develop strategies to achieve better business goals. This information can be about your brand's critics, the competition, current customers or customer feedback from new international markets.
Automatic transcription is also recent and in full development. It consists in converting an audio file into text.
Manual transcription is very long and requires a lot of patience. This is why many players have entered this market, which is now experiencing strong competition.
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Automatic summaries and reports
Beyond transcription, automatic reports and summaries are now available thanks to the progress of NLP. Artificial intelligence identifies the different speakers, extracts the actions and the keywords in order to produce a real report or summary tailored to your needs! All you have to do is to express your needs (report for more details or summary to keep a common thread) and the AI takes care of the rest.
Today, chatbots are present on a quarter of all company websites. They are still imperfect systems, but they are capable of handling standard tasks such as answering a customer question or informing them about a product/service. They are used through different channels such as the internet, social networks or messaging platforms.
Semantic search is actually ubiquitous. It concerns search engines, digital assistance agents, connected and smart speakers… This field is also in development and being improved.
Semantic search aims to improve the customer experience by providing individualized answers that clearly respond to the query expressed. It takes into account the context and compares the words used by the user with those in its database. This allows it to propose several answers corresponding to the request.
Here, the artificial intelligence will be able to extract key information in order to assign a category to the document in question. This can be done on the basis of "article topic" for example.
These advances reflect what AI encompasses and what it is capable of doing. However, this is just the beginning and these systems are expected to improve significantly over the next few years.
To get an idea of future applications, current projects include (among others):
- The human-machine interface (HMI)
The man-machine interfaces already exist, but they are in full mutation. It is a dashboard (screen) allowing the user to communicate with a computer program. For example, in a car, the driver can control the air conditioning, the lighting or the radio. These control instruments then refer to the car's HMI.
The HMI is an integral part of the customer experience. It is these interfaces that provide access to intuitive, modern and efficient platforms. The open platform architecture of tomorrow's HMI solutions will provide more functionality and application connectivity to ensure greater freedom of use.
Among the future projects of the HMI we also find the brain-to-text. As its name indicates, it would use brain waves to convert human thoughts into text.
But there is still a lot of work to do before we get there !
- No code platforms
No Code would allow digital content creators to develop more simply. There would be no need to code and the user's task would be more intuitive.
This means that the developer doesn't need to have too much technical knowledge, he can give free rein to his creativity and benefits from a better accessibility to the platforms. The idea is to improve and accelerate the creation of projects, as coding can sometimes be very time-consuming and requires a lot of knowledge.