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Robiul Islam
Jun 22, 2022
In Welcome to the Forum
So what is BERT really? BERT is described as a Banner Design pre-trained natural language deep learning framework that has shown state-of-the-art results on a wide variety of natural language processing tasks. During the research stages, and before being added to production research Banner Design systems, BERT achieved industry-leading results on 11 different natural language processing tasks. These natural language processing tasks include, but are not limited to, sentiment analysis, named entity determination, text entailment (or next sentence prediction), semantic role tagging, text classification, and the coreference resolution. BERT also helps disambiguate words with multiple meanings called polysemous words, in context. BERT is considered a model in many articles, however, it is more of a framework, as it provides Banner Design the foundation for machine learning practitioners to create their own BERT-like versions fine-tuned to meet a multitude of different tasks, and that's probably how Google implements it too. BERT was originally pre-trained on the Banner Design entire English Wikipedia and Brown Corpus and is refined on downstream natural language processing tasks like question and answer pairs. So it's not so much a one-time algorithmic change, but more of a foundational layer that seeks to help Banner Design understand and disambiguate linguistic nuances in phrases and sentences, continually adjust and to improve. The history of BERT To begin to realize the value that BERT brings, we need to look at previous developments. The natural language challenge Understanding how words fit together with structure and Banner Design meaning is a field of study related to linguistics. Natural Language Understanding (NLU), or NLP as it is otherwise known, dates back over 60 years, to the original Turing Test paper and definitions of what constitutes AI, and possibly earlier. . This compelling area is fraught with unresolved issues, many of which relate to Banner Design the ambiguous nature of language (lexical ambiguity). Almost every other word in the English language has multiple meanings. These challenges naturally extend to an ever-expanding web of content as search engines attempt to interpret intent to meet the informational needs expressed by users in written and spoken Banner Design queries. Lexical ambiguity In linguistics, ambiguity is at the level of the sentence rather than the word. Words with multiple meanings combine to make ambiguous phrases and sentences increasingly difficult to understand. According to Stephen Clark, formerly of the University of Cambridge, and Banner Design now a full-time researcher at Deepmind
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Robiul Islam

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