  To tackle Named Entity Recognition (NER) tasks, supervised methods need to
obtain sufficient cleanly annotated data, which is labor and time consuming. On
the contrary, distantly supervised methods acquire automatically annotated data
using dictionaries to alleviate this requirement. Unfortunately, dictionaries
hinder the effectiveness of distantly supervised methods for NER due to its
limited coverage, especially in specific domains. In this paper, we aim at the
limitations of the dictionary usage and mention boundary detection. We
generalize the distant supervision by extending the dictionary with headword
based non-exact matching. We apply a function to better weight the matched
entity mentions. We propose a span-level model, which classifies all the
possible spans then infers the selected spans with a proposed dynamic
programming algorithm. Experiments on all three benchmark datasets demonstrate
that our method outperforms previous state-of-the-art distantly supervised
methods.
