随着互联网的发展和移动用户的增加，互联网短文本急剧增长，影响日益广泛。因为标注简单、表达灵活、便于应用的优势，依存句法分析是目前句法分析的研究热点。但是针对网络文本，依存句法分析面临很大困难，主要难点之一是网络短文本存在大量语言成分省略问题，对依存句法分析提出了挑战。本项目针对网络短文本的语言成分省略问题，构建相应的依存句法分析系统，为网络文本分析和理解等应用探寻有效的技术手段。主要研究内容如下：(1)建立语言成分省略的描述体系，从表层到深层归纳为三个层次对问题进行描述；(2)研究网络文本中语言成分省略的机制和规律，提出一套语言成分识别和还原的标注规范，建立一定规模的语料库；(3)研究基于语言成分还原的依存句法分析框架，在结构化学习模型的基础上设计Coarse-to-Fine的语言成分还原架构；(4)针对大规模网络短文本数据，实现基于语言成分还原的依存句法分析系统，提高网络文本分析效果。

With the development of WWW and the increase of the mobile clients, there is a rapid and impactful increase of short web texts. Due to the simpler annotation, more flexible structures, and larger application impact, dependency parsing has been a hot research topic in the field of syntactic parsing. However, for web texts, dependency parsing faces a major issue, because web short texts contains lots of language information omitting phenomenons. This is a major challenge for dependency parsing with web texts. To deal with this problem, we study the language information omitting problem, and build a dependency parsing system that can capture such information for better natural language understanding on web texts. The major research points are as follows: (1) Build a three stage description system for language information omitting from surface to core layers. (2) Study the intrinsic rule and scheme of language information omitting, build an annotation strategy, and construct a corpus. (3) Study the language information omitting based dependency parsing framework, and propose a Coarse-to-Fine strategy for restoring the omitted language components. (4) For large-scale web text data, build the language information omitting based dependency parser to improve the accuracy of web text NLP systems.