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1. 发明授权

US08798984B2 Method and system for confidence-weighted learning of factored discriminative language models 有权
标题翻译：基于因子歧视语言模型的置信加权学习方法与系统
公开(公告)号：US08798984B2
公开(公告)日：2014-08-05
申请号：US13094999
申请日：2011-04-27
申请人： Nicola Cancedda , Viet Ha-Thuc
发明人： Nicola Cancedda , Viet Ha-Thuc
IPC分类号： G06F17/20 , G06F17/28 , G10L15/06
CPC分类号： G06F17/2818 , G10L15/06 , G10L15/197
摘要： A system and method for building a language model for a translation system are provided. The method includes providing a first relative ranking of first and second translations in a target language of a same source string in a source language, determining a second relative ranking of the first and second translations using weights of a language model, the language model including a weight for each of a set of n-gram features, and comparing the first and second relative rankings to determine whether they are in agreement. The method further includes, when the rankings are not in agreement, updating one or more of the weights in the language model as a function of a measure of confidence in the weight, the confidence being a function of previous observations of the n-gram feature in the method.
摘要翻译：提供了一种用于构建翻译系统的语言模型的系统和方法。该方法包括以源语言以相同源字符串的目标语言提供第一和第二翻译的第一相对排名，使用语言模型的权重确定第一和第二翻译的第二相对排名，该语言模型包括一组n-gram特征中的每一个的权重，并且比较第一和第二相对排名以确定它们是否一致。该方法还包括：当排名不一致时，将语言模型中的一个或多个权重作为权重中的置信度的函数来更新，所述置信度是n-gram特征的先前观察值的函数在该方法中。

2. 发明授权

US08484245B2 Large scale unsupervised hierarchical document categorization using ontological guidance 有权
标题翻译：使用本体论指导的大规模无监督层级文件分类
公开(公告)号：US08484245B2
公开(公告)日：2013-07-09
申请号：US13022766
申请日：2011-02-08
申请人： Viet Ha-Thuc , Jean-Michel Renders
发明人： Viet Ha-Thuc , Jean-Michel Renders
IPC分类号： G06F17/30
CPC分类号： G06F17/30705
摘要： A classification method includes constructing queries from category descriptors representing categories of a taxonomy of hierarchically organized categories. The query constructed for a category c includes a query component based on descriptors of the category c and at least one query component based on descriptors of an ancestor or descendant category of the category c. A documents database is queried using the constructed queries to retrieve pseudo-relevant documents. Language models for the categories of the taxonomy are extracted from the pseudo-relevant documents by inferring a hierarchical topic model representing the taxonomy. An input document is classified by optimizing mixture weights of a weighted combination of categories of the hierarchical topic model respective to the input document.
摘要翻译：分类方法包括从表示分级组织类别分类的类别的类别描述符构造查询。为类别c构造的查询包括基于类别c的描述符的查询组件和基于类别c的祖先或后代类别的描述符的至少一个查询组件。使用构造的查询查询文档数据库以检索伪相关文档。通过推断表示分类法的分层主题模型，从伪相关文档中提取分类法类别的语言模型。通过优化与输入文档相对应的分级主题模型的类别的加权组合的混合权重来分类输入文档。

3. 发明申请

US20120203752A1 LARGE SCALE UNSUPERVISED HIERARCHICAL DOCUMENT CATEGORIZATION USING ONTOLOGICAL GUIDANCE 有权
标题翻译：使用本体指导的大规模不均匀分类文档分类
公开(公告)号：US20120203752A1
公开(公告)日：2012-08-09
申请号：US13022766
申请日：2011-02-08
申请人： Viet Ha-Thuc , Jean-Michel Renders
发明人： Viet Ha-Thuc , Jean-Michel Renders
IPC分类号： G06F17/30
CPC分类号： G06F17/30705
摘要： A classification method includes constructing queries from category descriptors representing categories of a taxonomy of hierarchically organized categories. The query constructed for a category c includes a query component based on descriptors of the category c and at least one query component based on descriptors of an ancestor or descendant category of the category c. A documents database is queried using the constructed queries to retrieve pseudo-relevant documents. Language models for the categories of the taxonomy are extracted from the pseudo-relevant documents by inferring a hierarchical topic model representing the taxonomy. An input document is classified by optimizing mixture weights of a weighted combination of categories of the hierarchical topic model respective to the input document.
摘要翻译：分类方法包括从表示分级组织类别分类的类别的类别描述符构造查询。为类别c构造的查询包括基于类别c的描述符的查询组件和基于类别c的祖先或后代类别的描述符的至少一个查询组件。使用构造的查询查询文档数据库以检索伪相关文档。通过推断表示分类法的分层主题模型，从伪相关文档中提取分类法类别的语言模型。通过优化与输入文档相对应的分级主题模型的类别的加权组合的混合权重来分类输入文档。

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