  Prior methods to text segmentation are mostly at token level. Despite the
adequacy, this nature limits their full potential to capture the long-term
dependencies among segments. In this work, we propose a novel framework that
incrementally segments natural language sentences at segment level. For every
step in segmentation, it recognizes the leftmost segment of the remaining
sequence. Implementations involve LSTM-minus technique to construct the phrase
representations and recurrent neural networks (RNN) to model the iterations of
determining the leftmost segments. We have conducted extensive experiments on
syntactic chunking and Chinese part-of-speech (POS) tagging across 3 datasets,
demonstrating that our methods have significantly outperformed previous all
baselines and achieved new state-of-the-art results. Moreover, qualitative
analysis and the study on segmenting long-length sentences verify its
effectiveness in modeling long-term dependencies.
