  Existing approaches for named entity recognition suffer from data sparsity
problems when conducted on short and informal texts, especially user-generated
social media content. Semantic augmentation is a potential way to alleviate
this problem. Given that rich semantic information is implicitly preserved in
pre-trained word embeddings, they are potential ideal resources for semantic
augmentation. In this paper, we propose a neural-based approach to NER for
social media texts where both local (from running text) and augmented semantics
are taken into account. In particular, we obtain the augmented semantic
information from a large-scale corpus, and propose an attentive semantic
augmentation module and a gate module to encode and aggregate such information,
respectively. Extensive experiments are performed on three benchmark datasets
collected from English and Chinese social media platforms, where the results
demonstrate the superiority of our approach to previous studies across all
three datasets.
