The word embedding model provided by Word2Vec knows that words live together but does not understand in what context they should be used. True context is only Industry Email List 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 EL Mo trained on both the left and right side of a target word, these were done Industry Email List separately rather than 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 handle polysemy and homonyms. As a single word with Industry Email List multiple meanings is mapped to a single vector. Therefore, it is necessary to further disambiguate. We know there are many words 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 Industry Email List words have the same root origins and are extremely nuanced. Coreference resolution still problematic Search engines were still grappling with the difficult problem of resolving anaphors and cataphors,
Which was particularly problematic for conversational Industry Email List search and the assistant that can have questions and answers in multiple rounds. Being able to track 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 Industry Email List how does BERT help improve search engine language understanding? With these shortcomings above in mind, how has BERT helped search engines (and other researchers) understand the language?