Post by account_disabled on Mar 4, 2024 23:56:22 GMT -6
Since their appearance, computers have always operated on artificial languages. These languages were created to communicate instructions to machines, and as a result it was impossible for them to understand natural language, that is, the expressions that people actually use in their daily lives while speaking, writing or chatting. Human language, in fact, compared to formal language, is complex, diversified and has many nuances. Natural Language Processing (NLP) developed from this need, i.e. the field of research in the field of artificial intelligence which aims to develop models for understanding natural language, i.e. the language we use in everyday life. But, how do you teach a machine to talk? Language is “taught” to the machine at various levels of granularity: words, relationships between words and their use in a given context, syntactic dependencies, and semantic relationships.
This approach is integrated by automatic Venezuela Phone Number learning algorithms, i.e. Machine Learning, and Deep Learning ('deep learning'), which try to "imitate" the functioning of the human brain. Google's first big step in terms of natural language processing comes with the introduction of Hummingbird (in 2013) and RankBrain (in 2015). Hummingbird is a Core Update that demonstrated Google's commitment to obtaining an increasingly sophisticated understanding of the intent of search queries, in order to provide increasingly relevant results to the user. RankBrain operates under Hummingbird: it is a Deep Learning algorithm that uses mathematical vectors to transform language into entities understandable by a computer, and therefore help the core algorithm to better interpret user queries.
Google BERT: Bidirectional Encoder Representations from Transformers BERT , which stands for Bidirectional Encoder Representations from Transformers, is a neural network-based technique for natural language processing pre-training. Simply put, it can be used to help Google better discern the context of words in search queries. If the models prior to this update worked on individual words, in the exact order in which they were written, they were in fact designed in a unidirectional way (i.e., the meaning of a word in a context window could only move in direction, from left to right or from right to left, but never at the same time), BERT is capable of observing all the words of a sentence at the same time, in a bidirectional manner and therefore understanding how each single word influences all the others , like a human mind.
This approach is integrated by automatic Venezuela Phone Number learning algorithms, i.e. Machine Learning, and Deep Learning ('deep learning'), which try to "imitate" the functioning of the human brain. Google's first big step in terms of natural language processing comes with the introduction of Hummingbird (in 2013) and RankBrain (in 2015). Hummingbird is a Core Update that demonstrated Google's commitment to obtaining an increasingly sophisticated understanding of the intent of search queries, in order to provide increasingly relevant results to the user. RankBrain operates under Hummingbird: it is a Deep Learning algorithm that uses mathematical vectors to transform language into entities understandable by a computer, and therefore help the core algorithm to better interpret user queries.
Google BERT: Bidirectional Encoder Representations from Transformers BERT , which stands for Bidirectional Encoder Representations from Transformers, is a neural network-based technique for natural language processing pre-training. Simply put, it can be used to help Google better discern the context of words in search queries. If the models prior to this update worked on individual words, in the exact order in which they were written, they were in fact designed in a unidirectional way (i.e., the meaning of a word in a context window could only move in direction, from left to right or from right to left, but never at the same time), BERT is capable of observing all the words of a sentence at the same time, in a bidirectional manner and therefore understanding how each single word influences all the others , like a human mind.