Some of the really interesting things you’ll hear at the event are applications of large language models. There are two speakers who have been working on open source alternatives to GPT-3, publishing even bigger models and making them available to the community. We’ll also hear about Adaptive Testing of NLP models, NLP with Transfer Learning, and some exciting use cases of NLP in finance & insurance. Businesses use it to improve the search on a website, run chatbots or analyze clients’ feedback.
Named entity recognition is a core capability in Natural Language Processing (NLP). It’s a process of extracting named entities from unstructured text into predefined categories. Examples of named entities include people, organizations, and locations. The use of NLP has become more prevalent in recent years as technology has advanced. Personal Digital Assistant applications such as Google Home, Siri, Cortana, and Alexa have all been updated with NLP capabilities. These devices use NLP to understand human speech and respond appropriately.
Additionally, while all the sentimental analytics are in place, NLP cannot deal with sarcasm, humour, or irony. Jargon also poses a big problem to NLP – seeing how people from different industries tend to use very different vocabulary. NLP makes any chatbot better and more relevant for contemporary use, considering how other technologies are evolving and how consumers are using them to search for brands.
As far as categorization is concerned, ambiguities can be segregated as Syntactic (word-based), and Semantic (context-based). Startups planning to design and develop chatbots, voice assistants, and other interactive tools need to rely on NLP services and solutions to develop the machines with accurate language and intent deciphering capabilities. To advance some of the most promising technology solutions built with knowledge graphs, the National Institutes of Health (NIH) and its collaborators are launching the LitCoin NLP Challenge.
Because nowadays the queries are made by text or voice command on smartphones.one of the most common examples is Google might tell you today what tomorrow’s weather will be. But soon enough, we will be able to ask our personal data chatbot about customer sentiment today, and how we feel about their brand next week; all while walking down the street. Today, NLP tends to be based on turning natural language into machine language.
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