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.
Customer Impact | Major inconvenience |