  At present, the deep end-to-end method based on supervised learning is used
in entity recognition and dependency analysis. There are two problems in this
method: firstly, background knowledge cannot be introduced; secondly, multi
granularity and nested features of natural language cannot be recognized. In
order to solve these problems, the annotation rules based on phrase window are
proposed, and the corresponding multi-dimensional end-to-end phrase recognition
algorithm is designed. This annotation rule divides sentences into seven types
of nested phrases, and indicates the dependency between phrases. The algorithm
can not only introduce background knowledge, recognize all kinds of nested
phrases in sentences, but also recognize the dependency between phrases. The
experimental results show that the annotation rule is easy to use and has no
ambiguity; the matching algorithm is more consistent with the multi granularity
and diversity characteristics of syntax than the traditional end-to-end
algorithm. The experiment on CPWD dataset, by introducing background knowledge,
the new algorithm improves the accuracy of the end-to-end method by more than
one point. The corresponding method was applied to the CCL 2018 competition and
won the first place in the task of Chinese humor type recognition.
