书目名称 | Semi-Supervised Dependency Parsing |
编辑 | Wenliang Chen,Min Zhang |
视频video | http://file.papertrans.cn/865/864810/864810.mp4 |
概述 | Presents a comprehensive overview of semi-supervised approaches to dependency parsing.Bridges the gap between small human-annotated training data and huge raw data for dependency parsing.Explains why |
图书封面 |  |
描述 | This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown their advantages in the context of dependency parsing for many languages. Various semi-supervised dependency parsing approaches have been proposed in recent works which utilize different types of information gleaned from unlabeled data. The book offers readers a comprehensive introduction to these approaches, making it ideally suited as a textbook for advanced undergraduate and graduate students and researchers in the fields of syntactic parsing and natural language processing. |
出版日期 | Book 2015 |
关键词 | Big Data for Parsing; Dependency parsing; Dependency trees; Natural language processing; Parsing perform |
版次 | 1 |
doi | https://doi.org/10.1007/978-981-287-552-5 |
isbn_softcover | 978-981-10-1234-1 |
isbn_ebook | 978-981-287-552-5 |
copyright | Springer Science+Business Media Singapore 2015 |