Natural language processing

    What is Natural Language Processing? What role does NLP play in AI? You can find the answers here.

    What is natural language processing?

    Natural Language Processing (NLP) is the area of AI that enables computers to understand, process and generate human language in text or speech form. It is a combination of linguistic methods and machine learning (especially deep learning) to enable computers to deal with written or spoken information. NLP includes tasks such as analyzing the grammatical structure of a sentence, identifying key themes in a text or generating natural-sounding responses.

    Sub-areas and tasks of NLP

    NLP is very extensive. The classic subtasks include

    • Tokenization and parsing: dividing text into smaller units (words, sentences) and determining the grammatical structure (parts of speech, sentence tree).

    • Speech recognition (speech-to-text): Converting spoken language into written text using speech recognition algorithms.

    • Named entity recognition: Finding and categorizing names in texts (people, places, organizations, etc.).

    • Sentiment analysis: Determining whether a text is positively, negatively or neutrally colored - often used in the analysis of customer feedback or social media comments.

    • Machine translation: Automatic translation of texts from one language into another (e.g. Google Translate).

    • Question answering: Systems such as chatbots or search engines that provide precise answers to natural questions.

    • Text generation and summarization: Texts written by the model based on input, or automatic summaries of long texts.

    Methods

    Modern NLP systems use neural networks and pre-trained models. In the past, rule-based NLP with grammars created by linguists dominated. Today, models learn semantic and syntactic relationships from large text corpora. Techniques such as word embeddings (e.g. Word2Vec, GloVe) represent words as vectors, which enables machine "understanding" of similarities in meaning. Transformer models currently dominate NLP – above all BERT, GPT and co., which model context-related word meanings very well. As a result, NLP systems can now achieve impressive results, such as contextually accurate translations or answering complex questions in natural language.

    Applications

    Natural language processing permeates many everyday applications: Voice assistants (such as Siri/Alexa) use NLP to understand commands. Email programs use NLP techniques to detect spam. Customer service chatbots understand inquiries and help around the clock. Text analysis tools filter out important information from contract documents. And in the scientific field, NLP models help to find relevant publications or analyze large volumes of text (e.g. in the digital humanities). With the further development of LLMs, NLP has received a further boost – ChatGPT & Co demonstrate how natural and versatile human-machine dialogs can already be.

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