Natural language processing (NLP) is a field of Artificial intelligence that focuses on the interaction between computers and human language. The technology enables machines to analyse, interpret, and generate text in a way that humans can understand. NLP is used in a variety of fields, from automatic translators and chatbots, through sentiment analysis, to voice recognition and recommendation systems.
Natural Language Processing (NLP)
Type of technology
Description of the technology
Basic elements
- Tokenisation: Dividing text into smaller units, such as words or sentences.
- Part-of-speech tagging (POS tagging): Classification of words according to their grammatical function (noun, verb, etc.).
- Syntactic analysis: Determining the grammatical structure of a sentence.
- Semantics: Understanding the meaning of words in context.
- Language models: Algorithms that predict the next words or interpret the meaning of the text.
Industry usage
- Chatbots: Automated customer service through natural language interactions.
- Sentiment analysis: Monitoring customer sentiment on social media and product reviews.
- Machine translation: Automatic translation of texts from one language to another (e.g. Google Translate).
- Recommendation systems: Personalisation of product offers based on text analysis and customer preferences.
- Document processing: Automation of the analysis of legal documents, contracts, and reports.
Importance for the economy
NLP is significantly changing the way companies and organisations interact with customers and process information. By automating communication, document analysis, and recommendation systems, NLP reduces operational costs and improves the quality and speed of customer service. The technology is used in a wide range of sectors, from finance through medicine to marketing, and its further development will result in more efficient and automated operations in various industries.
Related technologies
Cloud Computing
NLP is widely used in mobile applications, customer service systems, and virtual assistants.
Big Data
The processing of large data sets and the training of NLP models take place in distributed computing environments.
Automation
It supports automated customer service systems and chatbots that communicate in natural language.
Mechanism of action
- Natural language processing is based on analysing textual data using algorithms and statistical models. Text is transformed into a structure that the system can understand – through tokenisation, part-of-speech tagging, and syntactic analysis. Machine learning models, such as LLMs (large language models), are used to analyse the relationship between words in context. NLP systems learn from large sets of textual data to understand language, generate responses, and automate communication-related processes.
Advantages
- Communication automation: It enables automatic generation of responses and interactions with users.
- Scalability: It enables the processing of huge amounts of text in a short period of time.
- Sentiment analysis: Facilitating the analysis of customer feedback and social media sentiment.
- Personalisation: It enables content customisation for individual user needs.
- Multilingualism: NLP supports machine translation and interaction in multiple languages.
Disadvantages
- Misinterpretations: NLP can misunderstand context or ambiguity in a text.
- Data privacy: Processing textual data, especially personal data, involves privacy risks.
- Disinformation: NLP can be used to generate false content or disinformation.
- Dependence on data quality: NLP models are only as good as the data they were trained on, which means that low-quality data can lead to errors.
- Implementation costs: Implementing advanced NLP systems can be costly and resource-intensive.
Implementation of the technology
Required resources
- Textual data sets: Textual data for training NLP models, such as articles, books, and documents.
- Computing power: Powerful servers for training large language models.
- Software: Tools and platforms for developing and implementing NLP models, such as spaCy, NLTK, and BERT.
- Team of specialists: AI engineers and NLP specialists to design and optimise algorithms.
- Computing environment: IT resources for real-time data processing.
Required competences
- Machine learning: Knowledge of NLP model training techniques, such as LSTM, BERT, and GPT.
- Natural language processing: Ability to analyse and transform textual data.
- Programming: Knowledge of NLP tools and frameworks, such as Python, TensorFlow, and PyTorch.
- Data analysis: Ability to prepare textual data and interpret it in context.
- Model optimisation: Ability to adapt models to specific applications and languages.
Environmental aspects
- Energy consumption: Training large NLP models requires considerable energy resources.
- Emissions of pollutants: Intensive data processing in data centres can lead to increased CO2 emissions.
- Raw material consumption: The required hardware resources for NLP data processing, such as servers and processors, may require rare raw materials.
- Recycling: NLP-related IT infrastructure upgrades and replacements generate electronic waste.
- Water consumption: Data centres needed to train NLP models can contribute to high water consumption in cooling processes.
Legal conditions
- Legislation governing the implementation of solutions such as AI Act (example: regulations on accountability for generated content).
- Environmental standards: Regulations for NLP processing data centre sustainability (example: regulations for data centre emissions).
- Intellectual property: Rules for protecting content generated and processed by NLP systems (example: copyright related to machine translation).
- Data security: Regulations for the protection of personal data used in NLP analyses (example: GDPR in the European Union).
- Export regulations: Regulations for the export of advanced natural language processing technologies (example: restrictions on exports to sanctioned countries).