Natural language processing (NLP) describes a branch of artificial intelligence (AI) that automates language recognition and generation so that computers and humans can communicate seamlessly. To interact with humans, computers must be adept at and understand syntax (grammar), semantics (word meaning), morphology (tense), and pragmatics (conversation). These tasks have proven quite complex.
Natural language processing encompasses machine learning tactics needed to process intricate transactions, including, among others, the following:
Using machine learning tactics, computers get NLP through natural language understanding (the ability to understand inputs) and natural language generation (the ability to formulate an appropriative narrative). As such, NLP includes not only speech recognition and generation, but also textual data.
Some Good Use Cases for NLP Include:
- Extracting data from complicated data sources
- Answering queries phrased in natural language
- Detecting phishing
- Making secure financial transactions
- Handling insurance transactions
- Assisting communication with/from those with disabilities
Other Definitions of NLP Include:
- “A branch of artificial intelligence” that extracts “interesting patterns in textual data, using a unique set of techniques.” (Charles Roe)
- “A form of artificial intelligence” that “helps machines understand, and even communicate with, human speech.” (Forbes)
- A type of technology that “combines artificial intelligence and computational linguistics so that computers and humans can talk seamlessly.” (Michelle Knight)
- “A branch of AI that enables computers to understand, process, and generate language just as people do.” (CIO).
- A technology that “involves the ability to turn text or audio speech into encoded, structured information, based on an appropriate ontology.” (Gartner)
Businesses Use NLP to:
- Improve customer service
- Assist in training
- Gain competitive edge
- Hire more qualified workers
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