Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.
Here are some of the key areas of NLP:
Sentiment Analysis: This involves analyzing text data to determine the writer’s attitude or emotions towards a particular topic. Sentiment analysis is commonly used to analyze social media data, customer feedback, and online reviews.
Text Classification: Text classification involves categorizing text data into predefined categories. This can be used to classify news articles, customer inquiries, and support tickets.
Named Entity Recognition: This involves identifying and classifying named entities in text, such as names, locations, and organizations. Named entity recognition is commonly used in information extraction and data mining.
Machine Translation: This involves using NLP to translate text from one language to another. Machine translation is commonly used for websites, documents, and customer support in multinational organizations.
Question Answering: This involves using NLP to answer natural language questions asked by users. This is commonly used in virtual assistants, chatbots, and search engines.
Text Generation: This involves using NLP to generate text based on input data. Text generation is commonly used in chatbots, language models, and content generation.
Working Of NLP:
NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning.
Some steps are:
Content categorization. A linguistic-based document summary, including search and indexing, content alerts and duplication detection.
Topic discovery and modeling. Accurately capture the meaning and themes in text collections, and apply advanced analytics to text, like optimization and forecasting.
Corpus Analysis. Understand corpus and document structure through output statistics for tasks such as sampling effectively, preparing data as input for further models and strategizing modeling approaches.
Contextual extraction. Automatically pull structured information from text-based sources.
Sentiment analysis. Identifying the mood or subjective opinions within large amounts of text, including average sentiment and opinion mining.
Speech-to-text and text-to-speech conversion. Transforming voice commands into written text, and vice versa.
Document summarization. Automatically generating synopses of large bodies of text and detect represented languages in multi-lingual corpora (documents).
Machine translation. Automatic translation of text or speech from one language to another.
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