Ability to auto tag entities based on the vocabulary/business glossary

Adding labels to the entity is tedious and time consuming effort. provide capability to auto tag based on the business glossary. or by referring business vocabulary.

some of the competitors are leveraging modified Maui - Multi-purpose automatic topic indexing a NLP based algorithm to bridge this gap in their product stacks.

Example: TopBraid Auto tagger https://www.topquadrant.com/products/topbraid-tagger-autoclassifier/

Poolparty Power tagging https://www.poolparty.biz/poolparty-powertagging

Maui NLP based open source algorithm being used in above products. It is enhancement to KEA - Key Phrase extraction alogirithm. Maui uses a lexical approach.

MAUI - https://github.com/zelandiya/maui

KEA - http://community.nzdl.org/kea/

Another interesting use case implemented by National Library of Finland.in their production env by leveraging this algorithm with other TF-IDF, facebook fastText model. It is called Annif. http://annif.org/ - you can try this in text analysis.

Maui uses a lexical approach, while TF-IDF, fastText represent different kinds of associative approaches

Article: https://www.liberquarterly.eu/article/10.18352/lq.10285/

RAKE - Rapid Automatic Keyword Extraction

PYTHON implementation - https://github.com/aneesha/RAKE

RAKE using NLTK - http://sujitpal.blogspot.com/2013/03/implementing-rake-algorithm-with-nltk.html

These can be used for both structured and unstructured data formats.