WHAT IS BERT ?
Googles latest search algorithm to easily understand natural language.
Growth in Natural Language Comprehension has been growing steadily over the last couple of years in machine learning models that process language. This success has led to BERT being revealed as one of the key forces behind Google’s Search Engine Optimisation. Google believes BERT to be “the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of Search”.
What is Google BERT?
What is BERT?
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a neural network-based technique for natural language processing pre-training. It belongs to the Search Engine Optimization tools which can be used to help Google better discern the context of words in search queries.
BERT is a natural language processing NLP framework that Google produced and then open-sourced to get better at natural language understanding. BERT is designed to help computers understand the meaning of ambiguous language in the text by using context-setting surrounding text. This framework was pre-trained using text from Wikipedia and can be fine-tuned with the question and answer datasets
BERT models can be particularly useful for understanding the intent behind search queries by considering the full context of a word by looking at the words that come before and after it.
For example, in the phrases “two to four” and “a quarter to three,” the word “to” has two different meanings, which is obvious to humans but not that obvious to the search engines. BERT is designed to distinguish between those differences in order to promote more meaningful tests.
HOW DOES BERT WORK?
The traditional way of training in language models works on the ordered sequence of words which goes from left-to-right or combined left-to-right and right-to-left. This is the point where BERT stands out with its bidirectional training method. BERT has the ability to train language models based on the entire set of words in a sentence or question instead of the conventional way of training on the ordered sequence of words.
BERT enables the language model to learn word meaning based on surrounding words rather than only the word immediately preceding or following them. Mask Language Mode and Next Sentence Prediction are two strategies used in BERT
The contextual representation of words starts from the very bottom of a deep neural network which makes BERT “deeply bidirectional”.
“For example, the word ‘bear, would have the same context-free representation in ‘polar bear ‘and ‘bear the pain’. Contextual models instead generate a representation of each word that is based on the other words in the sentence. For example, in the sentence ‘I saw a bear in the river,’ a unidirectional contextual model would represent ‘bear’ based on ‘I saw‘ but not ‘in the river.’ However, BERT represents ‘bear’ using both its previous and next context — ‘I saw the … in the river.’”
BERT’s application in SEO has given better impacts on Google Search. When we searched the title “drawing books for adults” Google surfaced results that listed drawing books for kids and children. But after the application of BERT, the surfaced results included books titled the same.
How does BERT helps in better search engine optimization?
BERT helps with things like:
1 Automatic summarisation
2. Conference Resolution
3.Named Entity Determination
6.Textual Entailment Next Sentence Prediction
7.Word sense Disambiguation
Why BERT stands out in SEO?
1.Helps google to understand human language better
BERT knowledge of the complexities of human language can make a big difference when it comes to how Google interprets queries as users are clearly searching for longer, challenging queries
2.Advancement of international SEO
The ability to convert mono-linguistic to multi-linguistic identities in BERT would attract more international users and help in translation as well
3.Helps scale conversational search
BERT will also have a huge impact on voice search and will make it more optimized
4.Help Google in better understanding of contextual differences and ambiguous queries
It would help Google in providing more accurate results by better SEO. It would thus solve the problem of lower rankings in queries and would help Google to be able to understand contextual nuance and ambiguous queries in a better way.
BERT is a milestone in the use of machine learning for natural language processing. The fact that it’s approachable and allows fast fine-tuning will likely allow a wide range of practical applications in the future making it the best Search Engine Optimization tool. BERT has inspired great interest in the NLP area. in particular in the application of the Transformer to NLP tasks. The original paper and Github repo associated open source you more details about BERT technical side.