Automated metadata Extraction supported by artificial intelligence
During pre-processing tokenization (a process of dividing your text into words) and part-of-speech tagging (classifying each word on its grammatical category) take place. This gets your text ready for further content analysis and metadata tagging.
EDIA’s entity extractor collects valuable keywords and named entities from your text. Keywords are words that have a meaning on their own such as force in physics or liver in biology. Named entities are proper names of people, organisations or products, such as Apple or Peter Smith.
EDIA’s readability analyzer provides an overview of the reading difficulty level of your texts. The reading difficulty of a text can be analysed through various difficulty scales such as CEFR, Flesch-Kincaid and others.
EDIA’s Topic Classifier identifies what your text is about from a taxonomy of predefined topical categories. EDIA supports IAB topical taxonomy. Other topical taxonomies such as curricula, learning objective taxonomies, and others are possible.
Our capabilities are delivered in the form of APIs or CMS (content management system) integrations that automatically extract valuable metadata from your educational content.
Other Capabilities in Development
We are continuously working on new capabilities that would automate processes in educational content development and delivery. See what AI functionality are currently being developed.
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