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