  Named entity recognition (NER) has been studied extensively and the earlier
algorithms were based on sequence labeling like Hidden Markov Models (HMM) and
conditional random fields (CRF). These were followed by neural network based
deep learning models. Recently, BERT has shown new state of the art accuracy in
sequence labeling tasks like NER. In this short article, we study various
approaches to task specific NER. Task specific NER has two components -
identifying the intent of a piece of text (like search queries), and then
labeling the query with task specific named entities. For example, we consider
the task of labeling Target store locations in a search query (which could be
entered in a search box or spoken in a device like Alexa or Google Home). Store
locations are highly ambiguous and sometimes it is difficult to differentiate
between say a location and a non-location. For example, "pickup my order at
orange store" has "orange" as the store location, while "buy orange at target"
has "orange" as a fruit. We explore this difficulty by doing multi-task
learning which we call global to local transfer of information. We jointly
learn the query intent (i.e. store lookup) and the named entities by using
multiple loss functions in our BERT based model and find interesting results.
