What is Cloud NLP?

What is Natural Language Processing?

Natural Language Processing (NLP) is a specialised branch of artificial intelligence that focuses on the interaction between computers and human languages. By using computational and linguistic techniques, NLP enables machines to understand, interpret, and generate human language in a meaningful and useful way. Many everyday applications, such as virtual assistants, chatbots, and text analytics tools, rely on NLP to process and derive insights from large volumes of unstructured textual and spoken data.

How does Natural Language Processing work?

NLP transforms unstructured text or spoken language into a structured form that computers can understand and act upon. It works through a series of complex algorithms and models that process and analyse human language.

• Tokenization divides text into smaller units called tokens. These tokens can be words, phrases, or even entire sentences. Tokenization simplifies the text, making it easier for further analysis and processing.
• Part-of-speech tagging identifies the grammatical parts of speech in a text, such as nouns, verbs, adjectives, and adverbs. This step is crucial for understanding the syntactic structure of the text, which can help in more advanced analyses.
• Named Entity Recognition (NER) identifies and classifies critical elements in a text, such as the names of people, organisations, locations, dates, and other significant entities. NER helps extract meaningful information from large text corpora.
• Parsing analyses the grammatical structure of a sentence to understand its meaning. This involves generating a parse tree that represents the syntactic structure of the text. Parsing is essential for understanding complex sentences and their interrelationships.
• Sentiment analysis determines the emotional tone or sentiment expressed in a piece of text. This technique is widely used to gauge public opinion and sentiment in social media monitoring, customer feedback analysis, and market research.
• Machine translation automatically converts text from one language to another. Modern machine translation systems use advanced NLP techniques such as neural networks and deep learning to improve accuracy and fluency, making it possible to translate complex texts in real time.
• Speech recognition converts spoken language into text. This involves analysing audio signals to detect phonemes, combining them to form words, and understanding the context to generate accurate transcriptions.
• Text classification sorts text into predefined categories or classes. This can be used for various applications, such as spam detection, topic categorisation, and sentiment analysis. Machine learning algorithms are often employed to improve the accuracy of text classification.
• Natural Language Generation (NLG) is the reverse of NLP, where computers generate human-like text based on given data or prompts. NLG is used for generating automated reports, chatbots, and content creation.