山西大学学报（自然科学版）2017，Vol.40Issue(3)：442-453,12.DOI:10.13451/j.cnki.shanxi.univ(nat.sci.).2017.03.007
基于深度学习的图解码依存分析研究进展
Advances in Graph-based Dependency Parsing Using Deep Learning
摘要
Abstract
Graph-based approach is one of the most successful approaches to dependency parsing.It is attractive for capability of offering global inference over space of all possible trees,and thus guarantees to find the best-scored trees given a tree scoring model.Traditional graph-based dependency parsing models usually adopt linear feature-based scoring models,which heavily rely on time-consuming feature engineering.The huge number of features they involves also dramatically slows down the parsing speed.Typical graph-based models factor the dependency tree into subgraphs,which limits the scope of feature extraction to the subgraph and inhibits the performance of recovering long distance dependencies.Recent work introduces deep learning models into graph-based dependency parsing models and seems to partially solve or alleviate the problems.In the paper Ⅰ survey some of this work and present the advances they have achieved.关键词
依存句法分析/图解码/深度学习Key words
dependency parsing/graph-based dependency parsing/deep learning分类
信息技术与安全科学引用本文复制引用
常宝宝..基于深度学习的图解码依存分析研究进展[J].山西大学学报（自然科学版）,2017,40(3):442-453,12.基金项目
973项目(2014CB340504) （2014CB340504）
国家自然科学基金(61273318) （61273318）
