  At present, most Natural Language Processing technology is based on the
results of Word Segmentation for Dependency Parsing, which mainly uses an
end-to-end method based on supervised learning. There are two main problems
with this method: firstly, the la-beling rules are complex and the data is too
difficult to label, the workload of which is large; secondly, the algorithm
cannot recognize the multi-granularity and diversity of language components. In
order to solve these two problems, we propose labeling rules based on phrase
windows, and designed corresponding phrase recognition algorithms. The labeling
rule uses phrases as the minimum unit, di-vides sentences into 7 types of
nestable phrase types, and marks the grammatical dependencies between phrases.
The corresponding algorithm, drawing on the idea of identifying the target area
in the image field, can find the start and end positions of various phrases in
the sentence, and realize the synchronous recognition of nested phrases and
grammatical dependencies. The results of the experiment shows that the labeling
rule is convenient and easy to use, and there is no ambiguity; the algorithm is
more grammatically multi-granular and diverse than the end-to-end algorithm.
Experiments on the CPWD dataset improve the accuracy of the end-to-end method
by about 1 point. The corresponding method was applied to the CCL2018
competition, and the first place in the Chinese Metaphor Sentiment Analysis
Task.
