Noun phrases reflect people’s understanding of the world entities and play an important role in people’s language system, conceptual system and application system.

With the Chinese “的(de)” structure, attributive noun phrases of the combined type can accommodate more words and syntactic structures, resulting in rich levels and complex semantic structures in Chinese sentences. Moreover, the Chinese elliptical “的(de)” structure is also of vital importance to the overall semantic understanding of the sentence. Many researches focus on rule-based models and semantic complement of “verb+的(de)” structure. To tackle these issues, we propose a general three-stage strategy utilizing neural network for the researches on all “的(de)” structure. Experimental results demonstrate that the proposed strategy is effective in boundary definition, elliptical recognition and semantic complement of “的(de)” structure.

名词短语反映人们对世界实体的理解，并在人们的语言系统，概念系统和应用系统中发挥重要作用。

借助汉语的“（de）”结构，组合类型的定语名词短语可以容纳更多的单词和句法结构，从而导致汉语句子中层次丰富且语义结构复杂。此外，中文的椭圆形的“（de）”结构对于句子的整体语义理解也至关重要。许多研究集中在基于规则的模型和“动词+的（de）”结构的语义补充上。为了解决这些问题，我们提出了一种利用神经网络的通用三阶段策略来研究所有“的（de）”结构。实验结果表明，该方法在边界定义，椭圆识别和“的（de）”结构语义补充方面是有效的。