搜寻结果

  1. Q

    结巴中文分词 词性标注 R 语言版

    结巴中文分词有很多语言的版本,详见 https://github.com/fxsjy/jieba 试了下 R 语言的结巴,好像还不错。步骤如下: 1. 安装结巴 > install.packages("jiebaR") 2. 读取库文件 > library(jiebaR) 3. 调用 worker() 函数 > tagger = worker("tag", symbol=T) 4. 分词、词性标注 > tagger <= "D:/ludingji.txt" 标注结果保存在 D 盘。
  2. Q

    R Fisher's Exact Test 脚本

    R 语言: Fisher's Exact Test 脚本 1. Why Fisher's Exact Test? Because Chi-squared Test is not so accurate when the expected frequency is less than 5. 2. How to use the script compute_fisher.r? It's very easy. Just copy all the code into R and change the fist line setwd() to the directory...
  3. Q

    第12届树库与语言学会议

    The Twelfth Workshop on Treebanks and Linguistic Theories (TLT12) 13th-14th December, Sofia, Bulgaria Co-event: The Third Workshop on Annotation of Corpora for Research in the Humanities
  4. Q

    双/多语语料库对齐软件 InterText

    http://wanthalf.saga.cz/intertext InterText 一款有意思的句级手工对齐软件。有以下功能: a. 可内嵌自动句对齐软件 hunalign 和 TAC 2 b. 对齐时可修改原文本内容和结构 c. 有客户端和服务器两个版本,支持同步(sync)。因此适用于多人的协同标注。
  5. Q

    资源分享:公开课 Web Intelligence and Big Data

    印度理工学校的公开课,介绍GOOGLE的一些幕后技术: https://class.coursera.org/bigdata-002/lecture/index 前两讲介绍了一些基本的搜索和统计算法,有 tf-idf, mutual information, bayes定理等,也算跟语料库有些关系吧。:)
  6. Q

    Science上的一篇文章Quantative Analysis of Culture Using Millions of Digitized Books

    Quantative Analysis of Culture Using Millions of Digitized Books we constructed a corpus of digitized texts containing about 4% of all books ever printed......
  7. Q

    语料库语言学及相关领域期刊列表

    维基百科条目 语料库语言学期刊 欢迎大家修改。
  8. Q

    制造术语 = 制造 ¥¥

    对比了机器学习和统计学,作者很幽默啊 http://www-stat.stanford.edu/~tibs/stat315a/glossary.pdf
  9. Q

    两款手工标注软件

    MAE (Multipurpose Annotation Environment) http://code.google.com/p/mae-annotation/ MAI (Multidocument Adjudication Interface) http://code.google.com/p/mai-adjudication/
  10. Q

    O'Reilly出的一本语料库的书

    难得O'Reilly还出有关语料库建设的书: Natural Language Annotation for Machine Learning A Guide to Corpus-Building for Applications http://shop.oreilly.com/product/0636920020578.do 好像还没正式出版,有个early release版,感兴趣的同志可以百毒一下。:)
  11. Q

    How to interpret a contingency table?

    Suppose we have the following data about the relationship between people's race and their belief in life after death (Agresti 2007 An Introduction to Categorical Data Analysis: 206) (This is another way of presenting a 3 x 2 contingency table) race belief freq 1 white yes...
  12. Q

    计量语言学

    刘海涛老师的一篇介绍计量语言学的短文: http://epaper.gmw.cn/gmrb/html/2012-02/15/nw.D110000gmrb_20120215_3-16.htm
  13. Q

    Larson-Hall's Statistics in SLA Using R

    http://cw.routledge.com/textbooks/9780805861853/guide-to-R.asp Jennifer Larson-Hall's companion guide of using R in SLA research to the book A Guide to Doing Statistics in Second Language Research Using SPSS
  14. Q

    R自然语言处理模块

    http://cran.r-project.org/web/views/NaturalLanguageProcessing.html 列举了R中许多与自然语言处理相关的模块。
  15. Q

    Good News!

    从2012年1月23日起,Manning和Jurafsky要在网上免费教授为期八周的自然语言处理课程,期待中。。。 http://www.nlp-class.org
  16. Q

    一个语言学者的控诉

    why we need good tools... Because we're not all interested in being R experts. By far, the single most frustrating part of my own graduate linguistics experience was the fact that in order to study the kinds of linguistic phenomena I wanted to, I had to spend most of my time learning...
  17. Q

    Manning的PPT

    介绍了很多自然语言处理的工具,pptx格式的,word2003的可以从网上下个插件打开: http://www-nlp.stanford.edu/~manning/courses/DigitalHumanities/DH2011-Manning.pptx
  18. Q

    Some of the best books about probability and statistics

    In empirical linguistics (such as corpus and computational linguistics), probability/statistics is a recurrent topic which cannot be circumvented. To understand this topic, many books tries to combine statistics and language studies in only one textbook. However, in my mind, it is better to read...
  19. Q

    Pitfalls in Corpus research

    http://lands.let.kun.nl/staff/staffPERperson.php3?name=rietveld
  20. Q

    信息检索书籍Introduction to Information Retrieval

    刚在网上闲逛发现了这个网站,作者是大名鼎鼎的manning噢: http://nlp.stanford.edu/IR-book/
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