Text QA
The Question Answering component answers a question based on a given context (e.g, a paragraph of text), where the answer to the question is a segment of the context.
Entity Extraction
Named Entity Recognition (NER) classifies tokens in text into predefined categories (tags), such as person names, quantity expressions, percentage expressions, names of locations, organizations, as well as expression of time, currency and others. We can recognize up to 19 entities. NER Beslogic also features a multilingual model that is available for 104 languages. NER Beslogic can be used as a knowledge extractor when you are interested in a piece of certain information in your text.
Language Detection
Detects what language a document or piece of text is written in
Text Classification
With the Classification endpoint, you can automatically classify large numbers of documents into a different category.
Article Extraction
Extract the main body of an article including embedded media such as links, images, videos etc. from any URL or Webpage
Summarization
Summarization allows you to take the important, relevant points and topics from a piece of text, making it easier to consume and analyze.