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Named entity recognition (NER) is a critical step in modern search queryunderstanding. In the domain of eCommerce, identifying the key entities, suchas brand and product type, can help a search engine retrieve relevant productsand therefore offer an engaging shopping experience. Recent research showspromising results on shared benchmark NER tasks using deep learning methods,but there are still unique challenges in the industry regarding domainknowledge, training data, and model production. This paper demonstrates anend-to-end solution to address these challenges. The core of our solution is anovel model training framework "TripleLearn" which iteratively learns fromthree separate training datasets, instead of one training set as istraditionally done. Using this approach, the best model lifts the F1 score from69.5 to 93.3 on the holdout test data. In our offline experiments, TripleLearnimproved the model performance compared to traditional training approacheswhich use a single set of training data. Moreover, in the online A/B test, wesee significant improvements in user engagement and revenue conversion. Themodel has been live on homedepot.com for more than 9 months, boosting searchconversions and revenue. Beyond our application, this TripleLearn framework, aswell as the end-to-end process, is model-independent and problem-independent,so it can be generalized to more industrial applications, especially to theeCommerce industry which has similar data foundations and problems.

An End-to-End Solution for Named Entity Recognition in eCommerce Search