Generative AI involves training a model using large datasets and algorithms, enabling it to produce near original contents.
NLP
AI transformations employ machine learning and deep learning models—for example, computer vision and natural language processing (NLP).
Application of NLP, data science, ML, and AI has changed the way we interact with computers, and it will continue to do so in the future.
NLP application holds significant value for businesses by dissecting human language, thereby facilitating automated analysis by machines.
From sentiment analysis and language translation to chatbots and text summarisation, the examples discussed in this blog.
Natural language processing consists of 5 steps machines follow to analyse, categorise, and understand spoken and written language.
The question of if NLP is supervised or unsupervised is not a binary one; it's a spectrum with various tasks falling along different points.
Discover how Artificial Intelligence enhances Natural Language Processing and its growing impact across different sectors.
Syntax, grammar variability, and linguistic diversity pose significant challenges for natural language processing (NLP) systems.
Noam Chomsky's transformational-generative grammar in the 1960s provided a theoretical framework for analysing syntactic structures.
NLP focuses on extracting meaning and context from text through semantic analysis and contextual understanding.
NLP powers language translation services like Google Translate and sentiment analysis tools for understanding public opinion.
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