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.
The commercial optimization software based on artificial intelligence must have feedback of the commercial actions carried out, of the nu [...]
Read More »As e-commerce continues to grow at a dizzying pace, fraudsters are also finding new and sophisticated ways to exploit the potential [...]
Read More »It is convenient that by means of a brief questionnaire we are able to verify the viability of a business opportunity. Next, develop [...]
Read More »In this article we are going to focus on how artificial intelligence (AI) can increase efficiency and reduce costs for your company by [...]
Read More »
