What is natural language processing (NLP)?
The importance of NLP in the information age
In an era of massive amounts of textual data generated every day (emails, articles, social media, documents), the ability of computers to automatically process and understand natural language is becoming extremely valuable. NLP makes it possible to extract knowledge from textual data, automate communication tasks and create more natural and intuitive user interfaces.
Basic tasks and techniques of NLP
NLP covers a wide range of tasks and techniques that can be divided into several levels of analysis:
- Morphological analysis: The study of word structure, variation (inflection) and construction (wordiness). Includes tokenization (dividing text into words/units), lemmatization (reducing words to their basic form) and stemming (reducing words to a stem), among others.
- Syntactic analysis (parsing): Examination of the grammatical structure of sentences, identification of parts of speech (POS tagging), recognition of phrases and relationships between words.
- Semantic Analysis: The study of the meaning of words, sentences and whole texts. Includes Named Entity Recognition (NER), Word Sense Disambiguation (WSD), entity relationship extraction, and sentiment analysis (assessing the emotional overtones of a text), among others.
- Discourse analysis: Examination of the structure and meaning of texts beyond single sentences, e.g., identifying links between sentences, recognizing argument structure.
- Natural Language Generation (NLG): Creating coherent and grammatically correct natural language texts based on data or internal knowledge representation.
Applications of NLP
NLP techniques find numerous applications in practice:
- Machine translation: automatic translation of texts between different languages (e.g. Google Translate).
- Sentiment analysis: Identify opinions and emotions expressed in texts (e.g., product reviews, social media posts).
- Chatbots and virtual assistants: creating conversational systems capable of engaging in dialogue with the user.
- Information retrieval and question answering: Question Answering (QA) systems that can find the answer to a natural language question in a large collection of documents.
- Information extraction: Automatically extracting structured information (e.g., names, places, dates) from unstructured texts.
- Text categorization and classification: Automatically assign texts to predefined categories (e.g. thematic, spam/non-spam).
- Speech recognition and generation: Speech-to-Text (STT) and Text-to-Speech (TTS) conversion.
NLP vs. machine learning and AI
Modern NLP relies heavily on machine learning techniques, especially deep learning (deep learning). Models such as recurrent networks (RNNs), LSTMs and, most notably, the Transformer architecture and large language models (LLMs) have revolutionized the capabilities of NLP, allowing it to perform significantly better on many tasks. NLP is a key component of many artificial intelligence systems.
Summary
Natural language processing (NLP) is a rapidly growing field that enables computers to interact with human language. Thanks to advances in machine learning, NLP is finding increasing applications, automating tasks, facilitating access to information and creating new ways for humans and machines to communicate.

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