Unlike English letters, Chinese characters have rich and specific meanings.
Usually, the meaning of a word can be derived from its constituent characters
in some way. Several previous works on syntactic parsing propose to annotate
shallow word-internal structures for better utilizing character-level
information. This work proposes to model the deep internal structures of
Chinese words as dependency trees with 11 labels for distinguishing syntactic
relationships. First, based on newly compiled annotation guidelines, we
manually annotate a word-internal structure treebank (WIST) consisting of over
30K multi-char words from Chinese Penn Treebank. To guarantee quality, each
word is independently annotated by two annotators and inconsistencies are
handled by a third senior annotator. Second, we present detailed and
interesting analysis on WIST to reveal insights on Chinese word formation.
Third, we propose word-internal structure parsing as a new task, and conduct
benchmark experiments using a competitive dependency parser. Finally, we
present two simple ways to encode word-internal structures, leading to
promising gains on the sentence-level syntactic parsing task.

提出了一种将中文单词的深度内部结构建模为具有 11 个标签的依赖树的方法，并使用一个竞争性的依存分析器进行基准实验，最后提出了两种编码中文单词深层结构的简单方法，从而在句子级依存结构分析任务上取得了有望的收益。