Results for tag: "Named Entity Recognition"

Named Entity Recognition (NER) is a sub-field of Natural Language Processing (NLP) that focuses on identifying and classifying named entities in text. NER systems are used to automatically extract information from unstructured text, such as people, organizations, locations, dates, and other entities. This technology is used in a variety of applications, such as information extraction, question answering, and text summarization. NER systems are typically trained using supervised machine learning algorithms, which require large amounts of labeled training data. This data is usually obtained from sources such as news articles, webpages, and other documents. There are a number of companies in the NER market, including IBM, Microsoft, Google, Amazon, and OpenText. These companies offer a range of NER solutions, from cloud-based services to on-premise software. Additionally, there are a number of open-source NER tools available, such as Stanford CoreNLP, spaCy, and NLTK. Show Less Read more