  State-of-the-art named entity recognition systems rely heavily on
hand-crafted features and domain-specific knowledge in order to learn
effectively from the small, supervised training corpora that are available. In
this paper, we introduce two new neural architectures---one based on
bidirectional LSTMs and conditional random fields, and the other that
constructs and labels segments using a transition-based approach inspired by
shift-reduce parsers. Our models rely on two sources of information about
words: character-based word representations learned from the supervised corpus
and unsupervised word representations learned from unannotated corpora. Our
models obtain state-of-the-art performance in NER in four languages without
resorting to any language-specific knowledge or resources such as gazetteers.
