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Leiden Weibo Corpus

Welcome to the home page of the Leiden Weibo Corpus, which consists of 5,103,566 messages posted on Sina Weibo. Sina Weibo is China's most popular microblogging service. And its 300 million users post 100 million messages a day. This corpus was designed to make it easier to explore the wealth of data these users generate. If you're interested, you can read about how the corpus was built. See which words are most frequently used on Sina Weibo. Look at a few random messages. Unknown (SW code: 0).

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Leiden Weibo Corpus | lwc.daanvanesch.nl Reviews

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Welcome to the home page of the Leiden Weibo Corpus, which consists of 5,103,566 messages posted on Sina Weibo. Sina Weibo is China's most popular microblogging service. And its 300 million users post 100 million messages a day. This corpus was designed to make it easier to explore the wealth of data these users generate. If you're interested, you can read about how the corpus was built. See which words are most frequently used on Sina Weibo. Look at a few random messages. Unknown (SW code: 0).

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1 leiden weibo corpus

2 messages

3 message id

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5 grammar help

6 region

7 běijīng 北京

8 tiānjīn 天津

9 héběi 河北

10 shānxī 山西

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Leiden Weibo Corpus | lwc.daanvanesch.nl Reviews

https://lwc.daanvanesch.nl

Welcome to the home page of the Leiden Weibo Corpus, which consists of 5,103,566 messages posted on Sina Weibo. Sina Weibo is China's most popular microblogging service. And its 300 million users post 100 million messages a day. This corpus was designed to make it easier to explore the wealth of data these users generate. If you're interested, you can read about how the corpus was built. See which words are most frequently used on Sina Weibo. Look at a few random messages. Unknown (SW code: 0).

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lwc.daanvanesch.nl

1

Leiden Weibo Corpus

http://lwc.daanvanesch.nl/help.php

Mistakes in the LWC. PMI (Pointwise mutual information). Difficulties viewing the LWC? Can't find what you are looking for? Feel free to send me an e-mail. To gather the data for this corpus, the Sina Weibo API. All messages were processed using tools from the Stanford Natural Language Processing Group. If you have any further questions on how the LWC was built, please feel free to get in touch. If you cite messages from the LWC, you can refer to their message IDs. These are unique identifiers which ...

2

Leiden Weibo Corpus

http://lwc.daanvanesch.nl/poweredby.php

The Leiden Weibo Corpus is powered by:. The Google Maps Utility Libraries. Java NLP tools from the Stanford Natural Language Processing Group. We owe a great debt of gratitude to everyone who contributed to these projects. Page generated in 0.0002 seconds. [ Home.

3

Leiden Weibo Corpus

http://lwc.daanvanesch.nl/about.php

This corpus was created by Daan van Esch. A graduate student of Chinese linguistics at Leiden University. The Netherlands. It contains 5.1 million messages from Sina Weibo that were posted in January 2012, along with linguistic annotations. You can read more about how this corpus was built here. Many thanks to my supervisor, Jeroen Wiedenhof. For some information on citing messages from the LWC. Page generated in 0.00017 seconds. [ Home.

4

Leiden Weibo Corpus

http://lwc.daanvanesch.nl/index.php

Welcome to the home page of the Leiden Weibo Corpus, which consists of 5,103,566 messages posted on Sina Weibo. Sina Weibo is China's most popular microblogging service. And its 300 million users post 100 million messages a day. This corpus was designed to make it easier to explore the wealth of data these users generate. If you're interested, you can read about how the corpus was built. See which words are most frequently used on Sina Weibo. Look at a few random messages. Unknown (SW code: 0).

5

Leiden Weibo Corpus

http://lwc.daanvanesch.nl/random.php

我烦死了，我宁愿闷着哭，我也不会和你发脾气我烦死啦，但是我宁愿闷着哭，我也不会和你发脾气我烦死啦，但是我宁愿闷着哭，我也不会和你发脾气我烦死啦，但是我宁愿闷着哭，我也不会和你发脾气我烦死啦，但是我哭，我也不会和你发脾气我烦死了但是我宁愿闷着哭，我也不会和你发脾气. 不管你信不信，反正我信了 / @小丸子很想念樱桃在德克萨斯扑克获得了成就初次胜利，获取速度超过了微博97%的玩家，你想和TA比试一下吗 http:/ t.cn/z0gzEE9. 可爱][太阳] 千部激情*片 http:/ t.cn/z0ezEfp [可怜][挖鼻屎] 现在和爱的人一起生活吗黑社会老爸画壁亮剑男儿本色情迷大话王欲望号列车那家伙的声音衣冠禽兽情迷大话王肉体与魔鬼松药店的儿子们我爱HK开心万岁我的野蛮女老师野鸭子上错花轿嫁对郎青木瓜之味魔鬼屠夫雪之女王. 人有三样东西是不该挥霍的身体、金钱和爱，你想挥霍，却得不偿失人有三样东西是无法挽留的生命、时间和爱，你想挽留，却渐行渐远人有三样东西是不该回忆的灾难、死亡和爱，你想回忆，却苦不堪言。 OT, 188,188,558 Record.

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HanziCraft - About HanziCraft

http://www.hanzicraft.com/about

New: See Phonetic Sets! Who is behind HanziCraft? HanziCraft is solely developed and maintained by Niel de la Rouviere. He blogs as Confused Laowai. Shares online Mandarin resources to the world via Social Mandarin. Brings language learning blogs together at Polyglot Link. And also learns other languages as a crazed linguaphile. Where does the data come from? HanziCraft would not exist without the massive amounts of work already done by other people and researchers. Leiden University Weibo Corpus Data.

unc.edu

Lexical databases and corpora

http://www.unc.edu/~jlsmith/lex-corp.html

Lexical databases and corpora. 183; UNC Chapel Hill. This is a list of links to lexical databases and corpora, organized by language or language group. The resources on this page were initially compiled from announcements on the LINGUIST list. And web-search results. This is not intended to be an exhaustive list, but rather a place to organize and store potentially useful links as I encounter them. Suggestions for additional links to include on this page are welcome. Contents of this page:. Includes tree...

sinoglot.com

Sinoglot | Who we are

http://sinoglot.com/who-we-are

Mdash; Language in Greater China. China means not just lots of languages but language families: Sinitic, Tibeto-Burman, Tungusic… If you expand the scope into history, scripts, bilingualism, language acquisition and so on, it’s enough to induce vertigo. Sinoglot is never going to cover it all, but we give you a bit in every dimension, eclectically. This is who we are:. Created the Leiden Weibo Corpus. He’s interested in language variation, both in 21st-century China and in the olden days. And Annals of Wu.

sinoglot.com

Sinoglot | Who we are

http://sinoglot.com/blog/who-we-are

Mdash; Language in Greater China. China means not just lots of languages but language families: Sinitic, Tibeto-Burman, Tungusic… If you expand the scope into history, scripts, bilingualism, language acquisition and so on, it’s enough to induce vertigo. Sinoglot is never going to cover it all, but we give you a bit in every dimension, eclectically. This is who we are:. Created the Leiden Weibo Corpus. He’s interested in language variation, both in 21st-century China and in the olden days. And Annals of Wu.

incomas-jyvaskyla.blogspot.com

-InCoMaS-: :: LWC: Leiden Weibo Corpus ::

http://incomas-jyvaskyla.blogspot.com/2012/04/lwc-leiden-weibo-corpus.html

LWC: Leiden Weibo Corpus :. It is my pleasure to announce to you today the Leiden Weibo Corpus (LWC),. An annotated linguistic 100-million word corpus containing 5.1 million. Messages from Sina Weibo, China's premier Twitter-like microblogging. The LWC is freely available online http:/ lwc.daanvanesch.nl/. The LWC was collected in January 2012. As such, it contains many linguistic. Phenomena that may not be found in older corpora, such as suffixation with. Ing", an aspectual marker borrowed from English.

chineseteachingtools.wordpress.com

Corpus Tools | A tool-box for teaching Chinese

https://chineseteachingtools.wordpress.com/corpus-tools

A tool-box for teaching Chinese. Notable bloggers and Personal Websites. Olympics, China, Nike commercial, Chinese Culture, Vintage. Tool to see words in context. Chinese Lunar New Year Greeting. Reading New York Times in Chinese. Beautiful blogs with Chinese themes. American Council on the Teaching of Foreign Languages (ACTFL). Center for Advanced Language Proficiency Education and Research (CALPER, Penn State University). Chinese Language Teachers Association (United States). Follow Blog via Email.

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lwc.daanvanesch.nl

Leiden Weibo Corpus

Welcome to the home page of the Leiden Weibo Corpus, which consists of 5,103,566 messages posted on Sina Weibo. Sina Weibo is China's most popular microblogging service. And its 300 million users post 100 million messages a day. This corpus was designed to make it easier to explore the wealth of data these users generate. If you're interested, you can read about how the corpus was built. See which words are most frequently used on Sina Weibo. Look at a few random messages. Unknown (SW code: 0).

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