  For a news content distribution platform like Dailyhunt, Named Entity
Recognition is a pivotal task for building better user recommendation and
notification algorithms. Apart from identifying names, locations, organisations
from the news for 13+ Indian languages and use them in algorithms, we also need
to identify n-grams which do not necessarily fit in the definition of
Named-Entity, yet they are important. For example, "me too movement", "beef
ban", "alwar mob lynching". In this exercise, given an English language text,
we are trying to detect case-less n-grams which convey important information
and can be used as topics and/or hashtags for a news. Model is built using
Wikipedia titles data, private English news corpus and BERT-Multilingual
pre-trained model, Bi-GRU and CRF architecture. It shows promising results when
compared with industry best Flair, Spacy and Stanford-caseless-NER in terms of
F1 and especially Recall.
