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<a class="post-title-link" href="/2018/07/01/杜成玉_语言开放典藏社群（OLAC）/" itemprop="url">语言开放典藏社群（OLAC）</a></h1>
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<p>提供者：杜成玉<br>下载地址：<a href="http://search.language-archives.org/index.html" target="_blank" rel="noopener">http://search.language-archives.org/index.html</a></p>
<h1 id="概述"><a href="#概述" class="headerlink" title="概述"></a>概述</h1><h2 id="数据来源：https-www-douban-com-note-269081724"><a href="#数据来源：https-www-douban-com-note-269081724" class="headerlink" title="数据来源：https://www.douban.com/note/269081724/"></a>数据来源：<a href="https://www.douban.com/note/269081724/" target="_blank" rel="noopener">https://www.douban.com/note/269081724/</a></h2><p>OLAC（Open Language Archives Community）<a href="http://search.language-archives.org/index.html语言开放典藏社群是由个人或组织所组成的国际性合作协会。许多种类的协会需要语言资源，如：语言学家、工程师、教师、演说家，也有许多机构提供片段性的架构，如：文件管理器、软件开发者和出版者。理论上，用户希望透过单一接口便可以取得任何需要的资源，其中资源种类涵盖：①资料（Data）：任何描述语言的相关信息；②工具（Tool）：有助于创造、浏览、查询或使用语言数据的计算器资源；③建议（Advice）：譬如，告知使用者什么资源具有高可靠度？在此情境中哪一种工具适合采用？当新的数据衍生出时该如何创造？但实际上，却有着语言资源散布在不同的网站、使用者无法得到想要的资源、语言资源在不同网站拥有不同名字（Name）造成召回率(recallrate)低，在其他领域有相同意义，造成正确率低（precision" target="_blank" rel="noopener">http://search.language-archives.org/index.html语言开放典藏社群是由个人或组织所组成的国际性合作协会。许多种类的协会需要语言资源，如：语言学家、工程师、教师、演说家，也有许多机构提供片段性的架构，如：文件管理器、软件开发者和出版者。理论上，用户希望透过单一接口便可以取得任何需要的资源，其中资源种类涵盖：①资料（Data）：任何描述语言的相关信息；②工具（Tool）：有助于创造、浏览、查询或使用语言数据的计算器资源；③建议（Advice）：譬如，告知使用者什么资源具有高可靠度？在此情境中哪一种工具适合采用？当新的数据衍生出时该如何创造？但实际上，却有着语言资源散布在不同的网站、使用者无法得到想要的资源、语言资源在不同网站拥有不同名字（Name）造成召回率(recallrate)低，在其他领域有相同意义，造成正确率低（precision</a> rate）、许多语言资源并非以文字为基础、不确定是否有建议适当软件，以及所提出的建议是否中肯…等问题。OLAC由此诞生。藉由下列步骤进行创造世界性语言资源的虚拟图书馆：针对语言资源数字典藏发展一致性的实践指引；发展网络上具有互操作性且提供存取相关语言资源的储存器（Repositories）和服务中心。</p>
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<a class="post-title-link" href="/2018/07/01/杜成玉_清华大学的汉语均衡语料库THACorpus/" itemprop="url">清华大学的汉语均衡语料库THACorpus</a></h1>
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<p>提供者：杜成玉<br>下载地址：<a href="http://www.umist.ac.uk/ctis/research/research-overview.htm" target="_blank" rel="noopener">http://www.umist.ac.uk/ctis/research/research-overview.htm</a></p>
<h1 id="概述"><a href="#概述" class="headerlink" title="概述"></a>概述</h1><h2 id="数据来源：https-www-douban-com-note-269081724"><a href="#数据来源：https-www-douban-com-note-269081724" class="headerlink" title="数据来源：https://www.douban.com/note/269081724/"></a>数据来源：<a href="https://www.douban.com/note/269081724/" target="_blank" rel="noopener">https://www.douban.com/note/269081724/</a></h2><p>翻译语料库方面则以英国曼彻斯特大学科技学院(UMIST) 翻译研究中心1995年创建的世界上第一个翻译语料库( Translational EnglishCorpus , TEC) 最为著名。该语料库主要收集从各国语言翻译成英语的文本,目前已有上千万词的语料(目标是5 千万词) ,分小说(约占80 %) 传记、报纸和期刊4 个子库。它并不要求必须双语对齐。该库不仅对语料进行了附码标注,还带有许多超语言信息的标注,如对译者情况(包括译者姓名、性别、民族、职业、翻译方向等) 、翻译方式、翻译类型、源语、原书情况、出版社等等均一一予以标注。</p>
<h1 id="相关论文"><a href="#相关论文" class="headerlink" title="相关论文"></a>相关论文</h1><p>[1]翟颖华. 面向第二语言教学的现代汉语核心词研究[D].武汉大学,2012.<br>[2]李素建. 汉语组块计算的若干研究[D].中国科学院研究生院（计算技术研究所）,2002.<br>[3]任海波. 现代汉语AABB重叠式词构成基础的统计分析[A]. 华中师范大学语言与语言教育研究中心.华中语学论库（第二辑）——汉语重叠问题[C].华中师范大学语言与语言教育研究中心:,2000:14.</p>
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<a class="post-title-link" href="/2018/06/30/朱述承_印度新闻标题数据集/" itemprop="url">印度新闻标题数据集</a></h1>
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2018-06-30
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<p>提供者：朱述承<br>访问地址：<a href="https://www.kaggle.com/therohk/india-headlines-news-dataset" target="_blank" rel="noopener">https://www.kaggle.com/therohk/india-headlines-news-dataset</a></p>
<h1 id="简介"><a href="#简介" class="headerlink" title="简介"></a>简介</h1><p>汇编了2001年至2017年印度“泰晤士报”发表的270万条新闻的标题。</p>
<h1 id="内容"><a href="#内容" class="headerlink" title="内容"></a>内容</h1><p>20010101 sports.wwe win over cena satisfying but defeating undertaker bigger roman reigns<br>20010102 bollywood Raju Chacha<br>20010102 unknown Status quo will not be disturbed at Ayodhya; says Vajpayee<br>20010102 unknown Fissures in Hurriyat over Pak visit<br>20010102 unknown America’s unwanted heading for India?<br>20010102 unknown For bigwigs; it is destination Goa<br>20010102 unknown Extra buses to clear tourist traffic<br>20010102 unknown Dilute the power of transfers; says Riberio<br>20010102 unknown Focus shifts to teaching of Hindi<br>20010102 unknown IT will become compulsory in schools<br>20010102 unknown Move to stop freedom fighters’ pension flayed<br>20010102 unknown Gilani claims he applied for passport 2 years ago<br>20010102 unknown Parivar dismisses PM’s warning<br>20010102 unknown India; Pak exchange lists of N-plants<br>20010102 unknown Will Qureshi’s return really help the govt?<br>20010102 unknown PM’s tacit message: Put Ram tample on hold<br>20010102 unknown Text of the Prime Minister’s article<br>20010102 unknown NCW to focus on violence against women<br>……</p>
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<link itemprop="mainEntityOfPage" href="http://yoursite.com/2018/06/30/朱述承_20万英语笑话文本数据集/">
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<a class="post-title-link" href="/2018/06/30/朱述承_20万英语笑话文本数据集/" itemprop="url">20万英语笑话文本数据集</a></h1>
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<p>提供者：朱述承<br>访问地址：<a href="https://github.com/taivop/joke-dataset" target="_blank" rel="noopener">https://github.com/taivop/joke-dataset</a></p>
<h1 id="简介"><a href="#简介" class="headerlink" title="简介"></a>简介</h1><p>这个数据库中有大约208000个笑话。</p>
<h1 id="文件"><a href="#文件" class="headerlink" title="文件"></a>文件</h1><p>目前数据集包含来自三个来源的笑话。<br>reddit_jokes.json | 195K jokes | 7.40M tokens<br>stupidstuff.json | 3.77K jokes | 396K tokens<br>wocka.json | 10.0K jokes | 1.11M tokens<br>TOTAL | 208K jokes | 8.91M tokens</p>
<h1 id="格式"><a href="#格式" class="headerlink" title="格式"></a>格式</h1><p>每个文件都是一个JSON文档，其中包含一个笑话对象的平面列表。每个笑话对象总是具有基于数据集的附加字段的正文字段。显然这些笑话并不全是好笑的；我们希望可以找到最好的，排序在相关的其他领域。请注意，这个标题在许多情况下都是笑话的一部分（特别是对于Reddit提交的笑话）。</p>
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<a class="post-title-link" href="/2018/06/30/朱述承_仇恨言语识别数据集/" itemprop="url">仇恨言语识别数据集</a></h1>
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<p>提供者：朱述承<br>访问地址：<a href="https://github.com/t-davidson/hate-speech-and-offensive-language" target="_blank" rel="noopener">https://github.com/t-davidson/hate-speech-and-offensive-language</a></p>
<h1 id="简介"><a href="#简介" class="headerlink" title="简介"></a>简介</h1><p>ICWSM 2017论文“自动仇恨语音检测和无礼语言问题”的作者贡献。包含3类短文本：a）包含仇恨言论；b）是冒犯性的，但没有仇恨言论；c）根本没有冒犯性。由15,000行文本构成，每个字符串都经过3人判断。</p>
<h1 id="大小"><a href="#大小" class="headerlink" title="大小"></a>大小</h1><p>3MB</p>
<h1 id="说明"><a href="#说明" class="headerlink" title="说明"></a>说明</h1><p>您可以在数据目录中找到我们标记的数据。我们已经将它们包含为pickle文件（Python 2.7）和CSV。您还可以在src目录中找到一个包含Python 2.7代码的笔记，以复制文章中的分析结果以及我们生成的词典目录中的词典，以便更准确地分类仇恨言论。分类器目录包含一个脚本，说明以及用于在新数据上运行分类器的必要文件，并提供了一个测试用例。</p>
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<a class="post-title-link" href="/2018/06/30/朱述承_Jeopardy数据集/" itemprop="url">Jeopardy数据集</a></h1>
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<p>提供者：朱述承<br>访问地址：<a href="https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/" target="_blank" rel="noopener">https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/</a></p>
<h1 id="简介"><a href="#简介" class="headerlink" title="简介"></a>简介</h1><p>包含216,930个危险问题。</p>
<h1 id="大小"><a href="#大小" class="headerlink" title="大小"></a>大小</h1><p>53MB</p>
<h1 id="数据来源"><a href="#数据来源" class="headerlink" title="数据来源"></a>数据来源</h1><p><a href="http://www.j-archive.com/" target="_blank" rel="noopener">http://www.j-archive.com/</a></p>
<h1 id="格式"><a href="#格式" class="headerlink" title="格式"></a>格式</h1><p>‘category’ : the question category, e.g. “HISTORY”<br>‘value’ : $ value of the question as string, e.g. “$200”<br>Note: This is “None” for Final Jeopardy! and Tiebreaker questions<br>‘question’ : text of question<br>Note: This sometimes contains hyperlinks and other things messy text such as when there’s a picture or video question<br>‘answer’ : text of answer<br>‘round’ : one of “Jeopardy!”,”Double Jeopardy!”,”Final Jeopardy!” or “Tiebreaker”<br>Note: Tiebreaker questions do happen but they’re very rare (like once every 20 years)<br>‘show_number’ : string of show number, e.g ‘4680’<br>‘air_date’ : the show air date in format YYYY-MM-DD</p>
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<a class="post-title-link" href="/2018/06/29/刘唯_The Wikipedia Corpus/" itemprop="url">The Wikipedia Corpus</a></h1>
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