  Many natural language understanding (NLU) tasks, such as shallow parsing
(i.e., text chunking) and semantic slot filling, require the assignment of
representative labels to the meaningful chunks in a sentence. Most of the
current deep neural network (DNN) based methods consider these tasks as a
sequence labeling problem, in which a word, rather than a chunk, is treated as
the basic unit for labeling. These chunks are then inferred by the standard IOB
(Inside-Outside-Beginning) labels. In this paper, we propose an alternative
approach by investigating the use of DNN for sequence chunking, and propose
three neural models so that each chunk can be treated as a complete unit for
labeling. Experimental results show that the proposed neural sequence chunking
models can achieve start-of-the-art performance on both the text chunking and
slot filling tasks.
