BERT has significantly impacted natural language processing (NLP) by redefining how machines understand human language. Its bidirectional processing allows for a nuanced understanding of context, leading to improved performance in tasks such as sentiment analysis and question answering [2]. Moreover, BERT has enhanced Google Search’s ability to understand complex queries, resulting in more accurate and relevant search results [2]. Its architecture supports versatility and widespread applications across various domains, setting new standards in NLP [5].

What is the impact of "BERT" in natural language processing?

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