According to the Emergen Research* report, the Natural Language Processing (NLP) market is expected to expand remarkably, from $12.43 billion in 2021 to $98.05 billion in 2030, with a compound annual growth rate of 25.7%. This dramatic growth reflects the revolution artificial intelligence (AI) has brought in the way machines interact with human language. At the heart of this transformation are the areas of Natural Language Processing (NLP), with two ‘sub-domains’ of Natural Language Understanding (NLU) and Natural Language Generation (NLG). It is therefore essential to understand in detail the components of this ever-changing market. This article explores each of these areas, providing an in-depth analysis of their functions, applications and importance in the evolution of AI.
Natural Language Processing (NLP)
Natural Language Processing (NLP) encompasses the methods and technologies that enable computers to read, understand and interpret human language. NLP focuses on the automatic processing of natural language in its raw form, often based on grammatical rules and statistical models. NLP can handle basic language processing tasks, but it does not necessarily understand the meaning or semantics of words and phrases. This is a broader field that includes NLU and NLG.
This technology is ubiquitous. Whether it is our connected objects, customer relationship processing or data research in finance, the addition of NLP technology is necessary to understand the text and exploit its full potential in all sectors of activity.
These three domains, while independent, are often interconnected in complex AI systems. For example, a voice assistant uses NLP to extract information, NLU to understand the meaning, and NLG to formulate a natural response.
Natural Language Generation (NLG)
In the NLG focuses the generation of a natural language from structured data (learn more). This is an essential step for human-machine interactions by making answers more accessible to the user.
Some applications of NLG
- Automatic reporting : Create summaries or reports from large amounts of data, such as financial or weather reports
- Chatbots: Generate natural responses in dialogues with users
- Creating marketing contents : Generate product descriptions, advertisements and personalized marketing campaigns (images, texts, etc.)
- Legal drafting : It can help lawyers by generating legal documents such as contracts and legal opinions
Natural Language Understanding (NLU)
NLU is a branch of AI that allows machines to understand and interpret human language as it is spoken or written. It goes beyond the simple recognition of language by the NLP, to grasp precisely the full meaning, including the context and intention behind the words or texts. (cf: Why the AI of Golem.ai is not a ctrl+f). The NLU requires a thorough understanding of human language and its subtleties to properly interpret the information contained in a text.
In addition, understanding natural language can be achieved through two distinct approaches: syntactic analysis and semantic analysis. Syntactic analysis involves examining language according to grammatical rules, meaning that it focuses on groups of words rather than individual words. Semantic analysis, on the other hand, is the process of capturing the meaning or logic of a statement.
Some applications of the NLU
- Automatic message processing : Analyze emails and their attachments, categorize messages, respond automatically…
- Form analysis Extraction of free texts from forms
- Intelligent voice assistants : Understanding voice requests (without transforming voice into text and vice versa, this is the role of the TTS and STT)
- sentiment analysis : Identify emotions in social media texts, reviews, etc.
- Systems for answering questions : Comprendre et répondre aux questions posées en langage naturel
- Analysis of satisfaction surveys : Identification of positive, negative or areas for improvement, identification of the services concerned…
- Insurance claims processing : data analysis to facilitate the work of actuaries or experts
NLU, a technology at the service of the trades
Natural Language Understanding is an artificial intelligence called analytical, which has very different uses of generative AI. Its role is not to generate text like the NLG, but to analyze unstructured data often present in large quantities in companies, and to understand its meaning.
Moreover, among NLU technologies two methods differ in their approach. A so-called “statistical” method that involves training on large volumes of data, a method called “Symbolic”, the technology of Golem.ai, which is based on rules and knowledge.
The advantage of a symbolic NLU technology makes it possible to avoid certain biases such as the lack of explainability and transparency, the massive consumption of (sometimes personal) data or sovereignty because it is not based on massive training. An advantage in many sectors where data is critical such as health, defense, finance etc.
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*https://www.actuia.com/actualite/le-marche-du-nlp-devrait-atteindre-plus-de-98-milliards-de-dollars-en-2030/
