RI-Small: Unsupervised Learning of Meaning

RI-Small：无监督意义学习

• 批准号: 0811974
• 负责人: Daniel Jurafsky
• 金额: $ 45万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811974&HistoricalAwards=false
• 关键词: RI; Small; Unsupervised; Learning; Meaning

The goal of this research to to develop a new generation of robust, highlyeffective unsupervised and semi-supervised models of meaningextraction. Our key methological insights include the use ofglobal methods of inference to simultaneously consider many linguisticaspects of the task, the use of rich typed lexical dependencies,and the semi-supervised use of structured data from the web, suchas dictionaries, thesauruses, encyclopedias, and so on. We areextracting meaning at three levels: word meaning, propositionalmeaning, and conceptual meaning. At the word level, we are learninglexical relations like hyponymy (leptin is-a hormone), synonymy,and others both from raw text and from structured sources like on-linedictionaries. At the propositional level, we are learningpredicate-argument structure using a global unsupervised clusteringmodel as well as developing semi-supervised methods of learning semantic frameextractors. At a larger structural level we are inducing scriptsand structured narrative relations between verbs. These rich modelsof meaning will have a broader impact by providing a critical steptowards the creation of systems with true language understandingcapabilities. The results could thus impact the creation ofnatural-language applications in every field, from educational ortutorial applications, to information extraction tasks like legaldiscovery, to conversational agents. All deliverables of thisproject will be available on the web: WordNet expansions, inducedframes and scripts, our temporal event classifier, and semanticrole, frame, and script inducers.

这项研究的目的是开发新一代的一系列强大的，高度有效的无监督和半监督的含义剥夺模型。 我们的关键选择性见解包括使用全球推理的方法同时考虑任务的许多语言学，使用丰富的打字词汇依赖性以及从网络中对结构化数据的半监督使用，Suchasas Dictionaries，Thesauruses，thesauruses，thesauruses，beyclopedias等。 我们在三个层面上摘录了含义：单词含义，命题，要求和概念含义。 在单词层面上，我们正在学习误解关系，例如sibymy（Leptin is-A激素），同义词以及其他来自原始文本和从结构化来源（如内在文典）等人。在命题层面上，我们使用全局无监督的簇模型以及开发半监督的学习语义framextractors的方法。 在较大的结构层面上，我们正在诱导动词之间的脚本和结构化的叙事关系。 这些丰富的含义模型将通过为具有真实语言理解障碍的系统的创建提供关键的steptowards，从而产生更大的影响。 因此，结果可能会影响每个领域中自然语言应用程序的创建，从教育待办事项应用程序到诸如LegalDiscovery的信息提取任务再到对话代理。 ThisProject的所有可交付成果都将在网络上可用：WordNet扩展，诱导FRAME和脚本，我们的时间事件分类器以及semanticrole，Frame和脚本诱导者。