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Ambiguity and context sensitivity are central challenges in NLP. Words can have multiple meanings depending on their context (polysemy), and disambiguating these meanings is crucial for accurate processing. For example, the word "bank" could refer to a financial institution or a riverbank, depending on its usage. Advanced NLP models like BERT and GPT-3 address this by using context-aware embeddings that capture word relationships within sentences. However, achieving human-level understanding in nuanced scenarios like sarcasm, idioms, or cultural references remains challenging. Such complexities highlight the limitations of current techniques and the importance of contextual analysis in real-world NLP applications. Why Other Options Are Incorrect :
What is LinkedIn?
World Wide Web is a collection of all information, resources, pictures, sounds and multimedia on the internet which is accessed through
.......... are set of rules and procedures to control the data transmission over the internet
Which PowerPoint view displays each slide of the presentation as a thumbnail and is useful for rearranging slides?
Which of the following type of languages are also known as Procedural Language?
Which of the following domains is used by Profit Business?
_______________ is used in second generation computer.
Codes consisting of lines of varying widths or lengths that are computer-readable are known as-
Which among the following is not an input device?
Trackball is a...........