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分布式证据语言的机器学习
Machine Learning of Language from Distributional Evidence |
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| 课程网址: |
http://videolectures.net/mitworld_manning_mlld/
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| 主讲教师: |
Chris Manning
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| 开课单位: |
斯坦福大学
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| 开课时间: |
2012-02-10
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| 课程语种: |
英语
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| 中文简介: |
克里斯托弗·曼宁认为, 语言学在20世纪误入歧途, 当时它在错误的假设下, 寻找 "语言的同质性, 只有同质的系统才能被构造". 面对人类创造性与语言的关系,严格的语言使用类别并不能解释人们的实际说话方式和他们选择说什么。对于每一个硬性和快速的规则语言学家发现, 其他语言学家可以确定一个例外。分类约束上升, 然后崩溃。 曼宁主张接受可变的语言系统, 并使用概率方法在这些系统中搜索结构。曼宁将定量技术应用于句子结构, 挖掘人们在某些现实世界中使用特定短语的频率、概率和可能性。从人们用一种语言表达思想的方式来看待分布, "可以更丰富地描述语言的使用方式", 事实上, 曼宁发现, 一种语言中对句子结构的某些典型约束 "显得更柔和了""manning 查看原始数据, 如《华尔街日报》的句子, 并将这些信息作为开始" 告诉我们动词和参数的依赖关系 "的典型单词关联。"他寻找单词之间的依赖关系, 它们之间的距离, 以及一个句子从左到右的流动。出现了类单词, 并对其进行了聚类, 产生了按分配学习的类别。某些类别的语法自然会同时出现。manning 构建嵌套短语结构树和分支结构, 并派生简单的概率模型, 帮助解释 "习得中的渐进学习和鲁棒性、个体的非均匀语法和渐变的语言变化"曼宁说, 计算语言学在信息检索、机器翻译和文本挖掘等应用领域也被证明是有用的。
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| 课程简介: |
Christopher Manning thinks linguistics went astray in the 20th century when it searched “for homogeneity in language, under the misguided assumption that only homogeneous systems can be structured.” In the face of human creativity with language, rigid categories of linguistic use just don’t help explain how people actually talk and what they choose to say. For every hard and fast rule linguists find, other linguists can determine an exception. Categorical constraints rise, then come crashing down. Manning argues for acceptance of variable systems of language, and for searching for structure in these systems using probabilistic methods. Manning applies quantitative techniques to sentence structure, digging for the frequency, probability and likelihood that people will use specific turns of phrase in certain real-world contexts. Looking at distributions in the ways people express ideas in a language “can give a much richer description of how language is used.” Indeed, Manning finds that certain typical constraints on sentence structure in one language “show up as softer constraints and preferences in other languages.” Manning looks at raw data, like sentences from the Wall Street Journal, and gleans such information as typical word associations that begin to “tell us about the dependencies of verbs and arguments.” He looks for dependencies between words, the distance between them, and at a sentence’s flow from left to right. Classes of words emerge, and clusters, yielding distributionally learned categories. Certain classes of syntax naturally fall together. Manning builds nested phrase structure trees, and branching structures, and derives simple probabilistic models that help explain “gradual learning and robustness in acquisition, non-homogeneous grammars of individuals, and gradual language change over time.” Manning says computational linguistics is also proving useful in such applied fields as information retrieval, machine translation, and text mining.
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| 关 键 词: |
计算语言学; 量化技术; 信息检索; 机器翻译; 文本挖掘
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| 课程来源: |
视频讲座网公开课
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| 最后编审: |
2020-05-18：王淑红(课程编辑志愿者)
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| 阅读次数: |
128
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