NiuTrans: An Open-Source Statistical Machine Translation System

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沈阳雅译网络技术有限公司

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News


NiuTrans Ver0.3.0 正式发布(2012.04.28)

朱老师参加中文信息学会2012战略研讨会(2012.04.12-16)

富士通中国研究中心孟遥博士一行来访(2012.03.23)

实验室参加日本东京NTCIR-9 PatentMT Workshop(2011.12.6-10)

实验室参加中国中文信息学会第七次全国代表大会(2011.12.4-5)

实验室庆祝成立31周年生日庆典(2011.12.01)

朱老师应邀参加日本宫崎中日双边研讨会(2011.10.28)

Sony和中兴通讯客人来访(2011.10.19)

实验室启动NiuTrans Server研制计划(2011.10.01)

实验室NiuTrans在NTCIR9 汉英专利翻译评测中取得好成绩(2011.09.25)

朱老师受邀参加了中日两国机器翻译技术合作研讨会(2011.09.25)

实验室参加了MT Summit XIII 和CWMT2011 (2011.09.19)

朱老师为ROCLing2011做大会特邀报告(2011.09.09)

朱老师应邀参加百度世界技术大会(2011.09.1)

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地址: 辽宁省沈阳市和平区3号巷11号
      东北大学 主楼410

邮箱:东北大学132# 计算机软件研究所
      自然语言处理实验室

邮编:110819

电话:024-83672481

传真:024-83672481

Email: niutrans(at)mail.neu.edu.cn

#132, Natural Language Processing Lab
Institute of Computer Software
College of Information Science & Engineering
Northeastern University, Shenyang, China.

110819

Tel:+86-24-83672481 Fax:+86-24-83672481



 
 


About us

Overview of NLP Group at Northeastern University, China

Founded in 1980 by Prof. Tianshun Yao and now led by Prof. Jingbo Zhu, Natural Language Processing Laboratory at Northeastern University (东北大学自然语言处理实验室), located in Shenyang China, has long been involved in the research of omputational linguistics, focusing on language parsing, text mining, and machine translation etc.


After over two decades' sustained work by its talented staff and students, NEU-NLPLab ranks among the top notch research bases with its high international prestige, advanced theoretical research and well-established industrial development capabilities.

Our research covers a range of topics in natural language processing (自然语言处理), including:

Machine Translation (机器翻译): Our current work in MT is focused on phrase-based and syntax-based SMT, involving string-to-tree and tree-to-string SMT. We have also studied example-based and pattern-based MT, and document-level translation techniques.

Language Parsing (语言分析): Our work in this area includes segmentation, POS tagging, chunking, semantic role labeling, relation extraction, syntactic parsing and semantic parsing.

Text Mining (文本挖掘): In this area we are focusing on text segmentation, classification, sentiment analysis and opinion mining, patent retrieval and mining, summarization, IE, and ontology learning.


 
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