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The BERT Neural Network
Technologies in the modern world are an integral part of human life. They affect almost all spheres of life, help make life easier, and add many new opportunities. To date, the Internet is the primary source for obtaining a variety of information, and generally speaking, Internet technologies provide a vast number of ways for modern people to develop. This paper will consider a new technology from Google – the BERT neural network, and its features and advantages, as well as the consequences of its application.
General Information
BERT is a neural network transformer model from Google, on which most of the automatic language processing tools are built today. The model appeared in early 2018, and in October of the same year, Google integrated the model into its search engine (Sur, 2020). BERT, or Bidirectional Encoder Representations from Transformers, is a neural network from Google that has shown some of the best results in solving many NLP tasks: from answering questions to machine translation. BERT models have already been pre-trained on large datasets (Nayak, 2019). Developers can download and embed a ready-made tool into their natural language processing projects without wasting time training a neural network from scratch.
The innovation of BERT lies in the method of pre-training. To learn, earlier architectures performed the task of generating text — that is, they predicted which word was most likely to be next, taking into account all the words before it. The decision of the neural network was influenced only by the words on the left, and such neural networks are called unidirectional (Nayak, 2019). To solve the problem, the developers came up with bidirectional neural networks. In short, two identical neural networks work in parallel, one predicts words from left to right, the other from right to left, and then the result of both networks is glued together.
A bidirectional neural network handles several tasks better than a unidirectional one, but it is still not precisely what is required. The developers get two “one-eyed” models that do not know what the other is doing (Sur, 2020). Therefore, BERT is pre-trained on a marked language model, and the essence is that it is necessary to predict the word not at the end of the sentence but somewhere in the middle.
Impact on SEO and Possible Optimization
As for the impact of the new search algorithm on websites and marketers, it is possible to say the following: BERT does not rank pages and does not send sites to the ban. The algorithm is designed exclusively to recognize the user’s request and will be helpful to everyone, including marketers and site owners (Huang et al., 2019). BERT will lead targeted visitors by accurately identifying long queries (Huang et al., 2019). There will be no more random people on the site, and the conversion rate will increase, which is good news for marketers. Unlike the Panda algorithm, which became a real revolution in SEO and forced many site owners to reconsider their attitude to texts, BERT only slightly improves the search due to intelligent query processing.
Another thing is essential: the algorithm opens up new opportunities for promotion. If earlier there were difficulties due to the specifics of the market segment, subject matter, company, or product name, now it will be easier, provided that the content is valuable and informative. Google claims that it is not necessary to optimize sites for the new algorithm. BERT was created to analyze queries and texts, understand their topics, and compare them with each other (Huang et al., 2019). The only recommendation that Google representatives give is to write naturally, which includes the absence of keywords artificially embedded in the text, compliance with the subject of the query, and informativeness.
Interaction
What is the next step Google can take to improve search algorithms?
References
Huang, W., Cheng, X., Wang, T., & Chu, W. (2019, October). BERT-based multi-head selection for joint entity-relation extraction. In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 713-723). Springer, Cham. Web.
Nayak, P. (2019). Understanding searches better than ever before. The Keyword. Web.
Sur, C. (2020). RBN: enhancement in language attribute prediction using global representation of natural language transfer learning technology like Google BERT. SN Applied Sciences, 2(1), 1-15. Web.
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