Natural language processing (NLP) is a revolutionary technology that enables machines to understand and communicate with human language, thanks to AI.
How can a computer, which usually understands a precise, marked-up and structured programming language, understand imprecise and ambiguous human language?
According to François Yvon, a researcher specialized in NLP, this technology refers to "all the research and development aimed at modeling and reproducing, with the help of machines, the human ability to produce and understand linguistic statements for communication purposes".
The ability to understand human language is not a new concept; as early as 1950, the famous mathematician Alan Turing organized a test that evaluated the intelligence of a machine by its ability to hold a human conversation.
It was during the 1950s that the first NLP tests were conducted. First, by the American government which wanted to decipher Soviet communications during the Cold War. Then, in 1954, by Georgetown University and IBM, who translated about sixty Russian sentences into English, making the first influential demonstration of machine translation.
Nevertheless, the resources invested remained very small and the results not very significant. Indeed, these first attempts were based on facts and rules that were difficult to understand the meaning and to manage the contextualization and ambiguities of human language.
NLP was widely criticized at the time and did not really take off until the arrival of machine learning and deep learning.
There are several ways of capturing human language: from already digitized texts or contents in an image, in a manuscript, by voice recognition or by extracting information from web pages, as search engines do.
The two main techniques used for natural language processing are syntactic analysis and semantic analysis.
Which itself includes :
- Parsing: which identifies the grammatical rules of a sentence and deciphers its meaning
- Word segmentation: which breaks down the text into units
- Morphological segmentation: which splits words into groups
Which consists in analyzing the context of a sentence to understand its meaning.
Today, natural language processing is one of the main drivers of AI and covers a very wide field of application:
This involves identifying subjective information in a text to extract the author's opinion. This makes it possible to measure the level of satisfaction of customers and users.
this is a software robot that will chat with an individual or a consumer through an automated conversation service. Chatbots work in different bricks: understanding the question, recognizing the words, determining the meaning and context, making the right decision, formulating the answer.
Machine translation algorithms that automate translation.
This involves organizing, structuring and categorizing a set of texts. This is particularly used in the context of content moderation, for example to detect fake news where the NLP will analyze keywords and compare articles to those of reliable sources to assess the credibility of information.
but also :
- Character recognition
- Automatic correction
- Automatic summarization
Although NLP is a major axis of artificial intelligence and this technology is revolutionizing certain business issues such as customer service, it is nevertheless confronted with various challenges. The understanding of human language remains complex because of :
Ambiguity: the different meanings of the same word depending on a given context, grammar...
Synonymy: there are more than 44 000 synonyms in the French language
Writing styles: depending on the author's emotions and intentions, the same idea can be expressed in different ways. The NLP will hardly be able to discern sarcasm or irony.
Human language is infinitely complex, even insoluble, and the machine still needs humans to grasp what remains elusive.
At isahit, we have chosen to make the link between humans and machines by setting up a technological and agile platform of artificial intelligence, augmented by human intelligence.
This choice of operation allows us to meet the challenges of NLP and to accompany our clients by guaranteeing a real understanding of the technology.
We have developed our own tool in order to better support our clients, who, like L'Oréal for example, have called on us to better qualify their data (discover our use case with L'Oréal).
In this article, our experts have gathered the best free text labeling tools for text annotation and categorization in Natural Language Processing. Enjoy your reading!
Discover the different use cases of natural language processing and the benefits for customer satisfaction!
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