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FudanNLP / Fnlp

Licence: lgpl-3.0
中文自然语言处理工具包 Toolkit for Chinese natural language processing

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FudanNLP (FNLP)

2018.12.16 我们很高兴发布了FudanNLP的后续版本,一个全新的自然语言处理工具FastNLP。FudanNLP不再更新。

2018.12.16 We are delighted to announce a new brand toolkit FastNLP, a major update of the FudanNLP. The FudanNLP is no longer updated.

====

介绍(Introduction)

FNLP主要是为中文自然语言处理而开发的工具包,也包含为实现这些任务的机器学习算法和数据集。 本工具包及其包含数据集使用LGPL3.0许可证。

FNLP is developed for Chinese natural language processing (NLP), which also includes some machine learning algorithms and [DataSet data sets] to achieve the NLP tasks. FudanNLP is distributed under LGPL3.0.

If you're new to FNLP, check out the Quick Start (使用说明) page.

原FudanNLP项目地址:http://code.google.com/p/fudannlp

功能(Functions)

	信息检索: 文本分类 新闻聚类
	中文处理: 中文分词 词性标注 实体名识别 关键词抽取 依存句法分析 时间短语识别
	结构化学习: 在线学习 层次分类 聚类

[ChangeLog 更新日志(ChangeLog)]
[性能测试(Benchmark)] (Benchmark) [开发计划(Development Plan)] (DevPlan) 开发人员列表(Developers)

Demos

你可以通过试用下面的网站来测试部分功能。 You can also use the following site to check the partial functionality. Demo Website(演示网站)

有遇到FNLP不能处理的例子,请到这里提交: 协同数据收集

有问题请查看FAQ或到 QQ群(253541693)讨论。

使用(Usages)

FNLP入门教程

除了源码文件,还需要下载FNLP模型文件。由于模型文件较大,不便于存放在源码库之中,请至Release页面下载,并将模型文件放在“models”目录。

  • seg.m 分词模型
  • pos.m 词性标注模型
  • dep.m 依存句法分析模型

欢迎大家提供非Java语言的接口。

引用(Citation)

If you would like to acknowledge our efforts, please cite the following paper. 如果我们的工作对您有帮助,请引用下面论文。

	Xipeng Qiu, Qi Zhang and Xuanjing Huang, FudanNLP: A Toolkit for Chinese Natural Language Processing, In Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2013.*


	@INPROCEEDINGS{Qiu:2013,
	author = {Xipeng Qiu and Qi Zhang and Xuanjing Huang},
	title = {FudanNLP: A Toolkit for Chinese Natural Language Processing},
	booktitle = {Proceedings of Annual Meeting of the Association for Computational Linguistics},
	year = {2013},
	}

这里 或 [DBLP](http://scholar.google.com/citations?sortby=pubdate&hl=en&user=Pq4Yp_kAAAAJ&view_op=list_works Google Scholar] 或 [http://www.informatik.uni-trier.de/~ley/pers/hd/q/Qiu:Xipeng.html) 可以找到更多的相关论文。

We used JProfiler to help optimize the code.

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