Natural Language Processing (NLP) is a branch of artificial intelligence focused on the interaction between computers and humans through natural language. Discover everything about this rapidly expanding field of research!
In the past, to interact with computers, we mainly used the keyboard and mouse. However, thanks to AI, machines can now understand, interpret, and respond intelligently to commands and texts provided by human users.
This feat is attributed to a technology called NLP: Natural Language Processing. With the rise of virtual assistants, automatic translation systems, chatbots, and many other applications, it is now in the spotlight!
Its history began with the first automatic translation tools that emerged in the 1950s, but it is only with advancements in Machine Learning and data processing that major progress has been made, driving many technological innovations…
So, what is NLP, what are its fundamentals, what algorithms are behind its functioning, and what are its applications? This is what you will discover in the rest of this article!
Data Analysis to Uncover the Secrets of Language
Complex and multifaceted, natural language consists of several concepts that must be understood and analyzed to enable automated processing.
Syntax concerns the structure of sentences and the rules governing the organization of words. It allows determining if a sentence is grammatically correct and identifying the relationships between its different elements.
On the other hand, semantics deals with the meaning of words and sentences. It is essential for understanding the meaning of texts and performing tasks such as lexical disambiguation and information extraction.
Another notion is pragmatics, which focuses on the use of language in context and on how contexts influence the interpretation of meaning.
Simply put, it is indispensable for understanding the intentions behind sentences and for managing aspects like irony and humor.
Morphology, on the other hand, is the study of the structure of words and their variations. It includes the analysis of prefixes, suffixes, roots, and inflections.
Finally, phonology concerns the sounds of language and their organization. It is more relevant for voice recognition and synthesis systems.
Thus, NLP encompasses a variety of tasks, each aiming to solve specific aspects of language understanding and generation.
Lexical analysis involves segmenting text into smaller units, such as words and tokens, and identifying their morphological properties.
Conversely, to determine the grammatical structure of sentences, syntactic analysis is used, allowing the construction of trees or syntactic dependencies.
A semantic analysis aims to understand the meaning of words and sentences, particularly through word disambiguation and interpreting semantic relationships.
Beyond analysis, NLP enables text generation: creating coherent and meaningful sentences and texts from structured data or other forms of internal representation.
It also allows for automatic translation, i.e., the conversion of texts from one language to another while maintaining the meaning and style as faithfully as possible.
It can also be used to summarize a long text by condensing it into a shorter version while preserving essential information and the overall meaning.
These different tasks form the foundation of NLP, on which advanced techniques and practical applications rest, which we will discuss next.
NLP Techniques and Algorithms
Statistical models and Machine Learning algorithms are the beating heart of modern NLP. They enable machines to learn patterns in textual data and perform various language processing tasks.
For example, classification and regression algorithms are used for tasks like part-of-speech tagging, sentiment analysis, and text classification.
Some of the most commonly used include support vector machines (SVM), random forests, and logistic regressions.
In parallel, language models such as n-grams, hidden Markov models (HMM), and Bayesian models are used to predict the probability of word sequences: a crucial process for voice recognition, spell checking, and text generation.
Before the era of Machine Learning, however, NLP systems were mainly rule-based. These approaches are less common today but remain useful in certain contexts.
Rule-based systems use sets of manually encoded grammatical rules to analyze and generate language. These include context-free grammars and finite state machines.
More recently, advances in Deep Learning have revolutionized NLP. They allow for far superior performance compared to traditional methods.
RNN (recurrent neural networks), especially LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), are used for sequential tasks like translation and text generation.
Similarly, CNN (convolutional neural networks) were initially developed for computer vision but have also been applied to NLP for tasks like sentiment analysis.
Transformer-based models, such as BERT and GPT, have also significantly improved NLP capabilities. They allow better understanding of context through bidirectional attention and are at the forefront of the latest advances.
What Is It Used For? What Are the Applications?
One of the key applications of NLP is sentiment analysis. It involves detecting emotions expressed in a text.
This is a very common practice in marketing, online reputation management, and consumer review studies.
Using classification algorithms and neural networks, NLP systems can identify positive, negative, or neutral sentiments in product reviews, social media comments, or news articles.
This allows companies to monitor public opinions, adapt their marketing strategies, and respond to customer needs.
Beyond social media and other customer feedback, NLP technologies are very useful for extracting and searching for relevant information in vast sets of textual data.
Search engines like Google use such algorithms to understand user queries and provide the most relevant results. The entire functioning of the internet relies on this system.
Moreover, virtual assistants like Siri, Alexa, or Google Assistant also rely on NLP. They use speech recognition technologies, natural language understanding, and response generation to interact with users smoothly and naturally.
The same technology is employed for chatbots, especially those automating customer service by providing instant answers to common questions. They help solve problems and thus speed up company operations.
However, the most well-known and widespread application of NLP is probably automatic translation. From the first rule-based systems to the latest generation neural translation models, considerable progress has been made.
Among the most popular tools in this field are Google Translate or DeepL. They use deep neural networks to produce translations that are as accurate as they are natural.
The Ambiguity of Natural Language: An Obstacle to Overcome
One of the main challenges of NLP is to handle the inherent ambiguity and complexity of human language. Words can have multiple meanings, and sentences can be interpreted in different ways depending on their structure.
For example, in English, the word “Bark” can refer to the outer layer of a tree or a dog’s sound. NLP systems, therefore, need to disambiguate these terms correctly based on the context.
The meaning of words and sentences can also change depending on context and culture. Idioms, idiomatic expressions, and cultural references pose additional challenges.
For example, the expression “kick the bucket” means “to die” in colloquial English, but its literal translation would be incomprehensible in another language without cultural knowledge.
Another problem is that NLP models can reflect and amplify biases present in the data on which they are trained, raising ethical concerns.
The textual data used for training can indeed contain cultural, social, and gender biases.
For example, if a model is primarily trained on texts in English from American sources, it might not understand or represent the cultures and dialects of other regions well.
Similarly, NLP systems can perpetuate stereotypes and discrimination. Therefore, methods need to be developed to identify and mitigate these biases to create fair and responsible systems.
On a large scale, natural language processing can also pose challenges in terms of computing resources and processing time.
Analyzing and understanding large amounts of textual data requires enormous computing power. Only the optimization of models allows their functioning without sacrificing precision.
Conclusion: NLP, a Revolution for Human-Machine Interactions
In the future, the field of NLP will continue to develop thanks to the emergence of new technologies. Future generative language models like GPT-5 and multimodal models, integrating text or image, will continue to push the limits.
The same goes for transfer learning techniques. Furthermore, NLP is increasingly integrated with other AI fields like computer vision and the Internet of Things.
Many industries like customer service, marketing, and education will likely be profoundly transformed.
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You know everything about NLP. For more information on the same topic, check out our complete article on NLP and our article on Deep Learning!
