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Named entity recognition (NER) is usually developed and tested on text fromwell-written sources. However, in intelligent voice assistants, where NER is animportant component, input to NER may be noisy because of user or speechrecognition error. In applications, entity labels may change frequently, andnon-textual properties like topicality or popularity may be needed to chooseamong alternatives.  We describe a NER system intended to address these problems. We test andtrain this system on a proprietary user-derived dataset. We compare with abaseline text-only NER system; the baseline enhanced with external gazetteers;and the baseline enhanced with the search and indirect labelling techniques wedescribe below. The final configuration gives around 6% reduction in NER errorrate. We also show that this technique improves related tasks, such as semanticparsing, with an improvement of up to 5% in error rate.

DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants