Abstract
In this work, we explore the way to perform
named entity recognition (NER) using only
unlabeled data and named entity dictionaries. To this end, we formulate the task as
a positive-unlabeled (PU) learning problem
and accordingly propose a novel PU learning
algorithm to perform the task. We prove
that the proposed algorithm can unbiasedly
and consistently estimate the task loss as if
there is fully labeled data. A key feature
of the proposed method is that it does not
require the dictionaries to label every entity
within a sentence, and it even does not require
the dictionaries to label all of the words
constituting an entity. This greatly reduces the
requirement on the quality of the dictionaries
and makes our method generalize well with
quite simple dictionaries. Empirical studies
on four public NER datasets demonstrate the
effectiveness of our proposed method. We
have published the source code at https://
github.com/v-mipeng/LexiconNER