  Sequence labeling is a fundamental task in natural language processing and
has been widely studied. Recently, RNN-based sequence labeling models have
increasingly gained attentions. Despite superior performance achieved by
learning the long short-term (i.e., successive) dependencies, the way of
sequentially processing inputs might limit the ability to capture the
non-continuous relations over tokens within a sentence. To tackle the problem,
we focus on how to effectively model successive and discrete dependencies of
each token for enhancing the sequence labeling performance. Specifically, we
propose an innovative attention-based model (called position-aware
selfattention, i.e., PSA) as well as a well-designed self-attentional context
fusion layer within a neural network architecture, to explore the positional
information of an input sequence for capturing the latent relations among
tokens. Extensive experiments on three classical tasks in sequence labeling
domain, i.e., partof-speech (POS) tagging, named entity recognition (NER) and
phrase chunking, demonstrate our proposed model outperforms the
state-of-the-arts without any external knowledge, in terms of various metrics.
