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Unlike English letters, Chinese characters have rich and specific meanings.Usually, the meaning of a word can be derived from its constituent charactersin some way. Several previous works on syntactic parsing propose to annotateshallow word-internal structures for better utilizing character-levelinformation. This work proposes to model the deep internal structures ofChinese words as dependency trees with 11 labels for distinguishing syntacticrelationships. First, based on newly compiled annotation guidelines, wemanually annotate a word-internal structure treebank (WIST) consisting of over30K multi-char words from Chinese Penn Treebank. To guarantee quality, eachword is independently annotated by two annotators and inconsistencies arehandled by a third senior annotator. Second, we present detailed andinteresting analysis on WIST to reveal insights on Chinese word formation.Third, we propose word-internal structure parsing as a new task, and conductbenchmark experiments using a competitive dependency parser. Finally, wepresent two simple ways to encode word-internal structures, leading topromising gains on the sentence-level syntactic parsing task.

An In-depth Study on Internal Structure of Chinese Words