  State-of-the-art Named Entity Recognition(NER) models rely heavily on large
amountsof fully annotated training data. However, ac-cessible data are often
incompletely annotatedsince the annotators usually lack comprehen-sive
knowledge in the target domain. Normallythe unannotated tokens are regarded as
non-entities by default, while we underline thatthese tokens could either be
non-entities orpart of any entity. Here, we study NER mod-eling with incomplete
annotated data whereonly a fraction of the named entities are la-beled, and the
unlabeled tokens are equiva-lently multi-labeled by every possible label.Taking
multi-labeled tokens into account, thenumerous possible paths can distract the
train-ing model from the gold path (ground truthlabel sequence), and thus
hinders the learn-ing ability. In this paper, we propose AdaK-NER, named the
adaptive top-Kapproach, tohelp the model focus on a smaller feasible re-gion
where the gold path is more likely to belocated. We demonstrate the superiority
ofour approach through extensive experimentson both English and Chinese
datasets, aver-agely improving 2% in F-score on the CoNLL-2003 and over 10% on
two Chinese datasetscompared with the prior state-of-the-art works.
