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Robiul Islam
Jun 22, 2022
In Welcome to the Forum
The word embedding model provided by Banner Design Word2Vec knows that words live together but does not understand in what context they should be used. True context is only possible when all the words in a sentence are taken into consideration. For example, Word2Vec doesn't know when river (shore) is the right context, or bank (deposit). While later models such as ELMo trained on both the left and right side of a target word, these were done separately rather than Banner Design looking at all the words (left and right) simultaneously, and still haven't provided any real context. . Poorly managed polysemy and homonymy Word integrations like Word2Vec do not properly Banner Design handle polysemy and homonyms. As a single word with multiple meanings is mapped to a single vector. Therefore, it is necessary to further disambiguate. We know there are many words Banner Design with the same meaning (e.g. 'run' with 606 different meanings) so this was a shortcoming. As illustrated earlier, polysemy is particularly problematic because polysemous words have the same root origins and are extremely nuanced. Coreference resolution still problematic Search engines were still grappling with the difficult problem of Banner Design resolving anaphors and cataphors, which was particularly problematic for conversational search and the assistant that can have questions and answers in multiple rounds. Being able to track Banner Design the entities referred to is essential for these types of voice queries. Shortage of training data Modern deep learning-based NLP models learn best when trained on huge amounts of annotated training examples, and lack of training data was a common problem holding back the research field as a whole. . So how does BERT help improve search engine language understanding? With these shortcomings above in mind, how has Banner Design BERT helped search engines (and other researchers) understand the language? What makes BERT so special? There are several things that make BERT so special for research and beyond (the world - yes, that's as big as a research base for natural language processing). Many of the special features can be found in the title of the BERT article - BERT: Bi-directional Encoder Representations from Transformers. B - bidirectional Banner Design E - Encoder R - Representations T - Transformers But there are other exciting developments that BERT brings to the field of natural language understanding. These include: Pre-training from unlabeled text Two-way context models The use of a transformer architecture Hidden language modeling Focused attention Text implication (next sentence prediction) Disambiguation through open-source context
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Robiul Islam

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