  Named entity recognition, and other information extraction tasks, frequently
use linguistic features such as part of speech tags or chunkings. For languages
where word boundaries are not readily identified in text, word segmentation is
a key first step to generating features for an NER system. While using word
boundary tags as features are helpful, the signals that aid in identifying
these boundaries may provide richer information for an NER system. New
state-of-the-art word segmentation systems use neural models to learn
representations for predicting word boundaries. We show that these same
representations, jointly trained with an NER system, yield significant
improvements in NER for Chinese social media. In our experiments, jointly
training NER and word segmentation with an LSTM-CRF model yields nearly 5%
absolute improvement over previously published results.
