Named Entity Recognition (NER) is a Natural Language Processing (NLP) technique that consists of identifying and classifying named entities in a text.
Named entities can be any real-world object that has a name of its own, such as people, organisations, locations, dates, times, currencies, among others.
The goal of NER is to identify these entities in a text and classify them into different categories, which can be useful in applications such as sentiment analysis, information extraction, text summarisation, among others.
NER is based on machine learning algorithms that analyse the linguistic features of a text to identify patterns and make decisions about the presence and classification of named entities in it.
Since 2008, several countries have enacted legislation that recognizes the importance of integrating artificial intelligence (AI) into key areas of life [...]
Read More »After the revolutions led by coal, electricity, and then electronics, society is now witnessing a fourth revolution in the energy sector.
Read More »The rise of Artificial Intelligence (AI) in business is very topical. Its use is spreading and is changing, even, the models [...]
Read More »To know how semantic technology works, the first thing you need to know is that it is responsible for helping artificial intelligence systems [...]
Read More »
