  Named entity recognition (NER) is a critical step in modern search query
understanding. In the domain of eCommerce, identifying the key entities, such
as brand and product type, can help a search engine retrieve relevant products
and therefore offer an engaging shopping experience. Recent research shows
promising results on shared benchmark NER tasks using deep learning methods,
but there are still unique challenges in the industry regarding domain
knowledge, training data, and model production. This paper demonstrates an
end-to-end solution to address these challenges. The core of our solution is a
novel model training framework "TripleLearn" which iteratively learns from
three separate training datasets, instead of one training set as is
traditionally done. Using this approach, the best model lifts the F1 score from
69.5 to 93.3 on the holdout test data. In our offline experiments, TripleLearn
improved the model performance compared to traditional training approaches
which use a single set of training data. Moreover, in the online A/B test, we
see significant improvements in user engagement and revenue conversion. The
model has been live on homedepot.com for more than 9 months, boosting search
conversions and revenue. Beyond our application, this TripleLearn framework, as
well as the end-to-end process, is model-independent and problem-independent,
so it can be generalized to more industrial applications, especially to the
eCommerce industry which has similar data foundations and problems.
