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1. 发明授权

US07277732B2 Language input system for mobile devices 有权
标题翻译：移动设备语言输入系统
公开(公告)号：US07277732B2
公开(公告)日：2007-10-02
申请号：US09843358
申请日：2001-04-24
申请人： Zheng Chen , Mingjing Li , Feng Zhang , Rui Yang , Jianfeng Gao
发明人： Zheng Chen , Mingjing Li , Feng Zhang , Rui Yang , Jianfeng Gao
IPC分类号： A04B1/38
CPC分类号： G06F3/0236 , G06F3/018 , G06F3/0237 , H04M1/72519 , H04M2250/58 , H04M2250/70
摘要： A language system facilitates entry of an input string into a mobile device using discrete keys on a keypad, such as a 10-key keypad. The numeric keys have associated letters of an alphabet. The key input is representative of one or more Chinese phonetic characters. Based on this input string, the language system derives the most likely Chinese corresponding language characters intended by the user. The language system uses multiple different search engines and language models to aid in deriving the most probable Chinese language characters. When the language system recognizes possible Chinese language characters, the mobile device displays the possible Chinese language characters for user selection of the possible Chinese language characters and/or further input of one or more Chinese phonetic characters. In this manner, the language system adopts a modeless entry methodology that eliminates conventional mode switching between input and selection operations.
摘要翻译：语言系统有助于使用键盘上的离散键（诸如10键键盘）将输入串输入到移动设备中。数字键具有字母的相关字母。关键输入是一个或多个汉语拼音字符的代表。基于该输入字符串，语言系统导出用户想要的最可能的中文对应语言字符。语言系统使用多种不同的搜索引擎和语言模型来帮助推导出最可能的中文字符。当语言系统识别可能的中文字符时，移动设备显示可能的汉语字符，用于选择可能的中文字符和/或进一步输入一个或多个汉语拼音字符。以这种方式，语言系统采用无模式输入方法，消除了输入和选择操作之间的常规模式切换。

2. 发明授权

US07124080B2 Method and apparatus for adapting a class entity dictionary used with language models 有权
公开(公告)号：US07124080B2
公开(公告)日：2006-10-17
申请号：US10008432
申请日：2001-11-13
申请人： Zheng Chen , Jianfeng Gao , Mingjing Li , Feng Zhang
发明人： Zheng Chen , Jianfeng Gao , Mingjing Li , Feng Zhang
IPC分类号： G10L15/06 , G10L15/00
CPC分类号： G06F17/2715 , G06F17/2775
摘要： A method and apparatus are provided for augmenting a language model with a class entity dictionary based on corrections made by a user. Under the method and apparatus, a user corrects an output that is based in part on the language model by replacing an output segment with a correct segment. The correct segment is added to a class of segments in the class entity dictionary and a probability of the correct segment given the class is estimated based on an n-gram probability associated with the output segment and an n-gram probability associated with the class. This estimated probability is then used to generate further outputs.

3. 发明授权

US07275029B1 System and method for joint optimization of language model performance and size 有权
标题翻译：联合优化语言模型性能和尺寸的系统和方法
公开(公告)号：US07275029B1
公开(公告)日：2007-09-25
申请号：US09607786
申请日：2000-06-30
申请人： Jianfeng Gao , Kai-Fu Lee , Mingjing Li , Hai-Feng Wang , Dong-Feng Cai , Lee-Feng Chien
发明人： Jianfeng Gao , Kai-Fu Lee , Mingjing Li , Hai-Feng Wang , Dong-Feng Cai , Lee-Feng Chien
IPC分类号： G06F17/27
CPC分类号： G06F17/2735 , G06F17/274 , G06F17/2818
摘要： A method for the joint optimization of language model performance and size is presented comprising developing a language model from a tuning set of information, segmenting at least a subset of a received textual corpus and calculating a perplexity value for each segment and refining the language model with one or more segments of the received corpus based, at least in part, on the calculated perplexity value for the one or more segments.
摘要翻译：提出了一种用于联合优化语言模型性能和大小的方法，包括从调整的信息集开发语言模型，分割所接收的文本语料库的至少一个子集，并计算每个分段的困惑度值，并用至少部分地基于所计算的一个或多个段的困惑度值，所接收的语料库的一个或多个段。

4. 发明授权

US07254529B2 Method and apparatus for distribution-based language model adaptation 有权
标题翻译：基于分布式语言模型适应的方法和装置
公开(公告)号：US07254529B2
公开(公告)日：2007-08-07
申请号：US11225543
申请日：2005-09-13
申请人： Jianfeng Gao , Mingjing Li
发明人： Jianfeng Gao , Mingjing Li
IPC分类号： G06F17/27 , G06F17/28 , G10L15/00
CPC分类号： G06F17/2715 , G10L15/065 , G10L15/1815
摘要： A method and apparatus are provided for adapting a language model to a task-specific domain. Under the method and apparatus, the relative frequency of n-grams in a small training set (i.e. task-specific training data set) and the relative frequency of n-grams in a large training set (i.e. out-of-domain training data set) are used to weight a distribution count of n-grams in the large training set. The weighted distributions are then used to form a modified language model by identifying probabilities for n-grams from the weighted distributions.
摘要翻译：提供了一种用于使语言模型适应于任务特定领域的方法和装置。在该方法和装置下，小训练集中的n-gram的相对频率（即任务特定的训练数据集）和大训练集中的n-gram的相对频率（即，域外训练数据集）用于在大训练集中加权n-g的分布计数。然后通过从加权分布中识别n克的概率，将加权分布用于形成修改后的语言模型。

5. 发明授权

US07043422B2 Method and apparatus for distribution-based language model adaptation 失效
公开(公告)号：US07043422B2
公开(公告)日：2006-05-09
申请号：US09945930
申请日：2001-09-04
申请人： Jianfeng Gao , Mingjing Li
发明人： Jianfeng Gao , Mingjing Li
IPC分类号： G06F17/27
CPC分类号： G06F17/2715 , G10L15/065 , G10L15/1815
摘要： A method and apparatus are provided for adapting a language model to a task-specific domain. Under the method and apparatus, the relative frequency of n-grams in a small training set (i.e. task-specific training data set) and the relative frequency of n-grams in a large training set (i.e. out-of-domain training data set) are used to weight a distribution count of n-grams in the large training set. The weighted distributions are then used to form a modified language model by identifying probabilities for n-grams from the weighted distributions.

6. 发明申请

US20060009965A1 Method and apparatus for distribution-based language model adaptation 有权
公开(公告)号：US20060009965A1
公开(公告)日：2006-01-12
申请号：US11225543
申请日：2005-09-13
申请人： Jianfeng Gao , Mingjing Li
发明人： Jianfeng Gao , Mingjing Li
IPC分类号： G06F17/27
CPC分类号： G06F17/2715 , G10L15/065 , G10L15/1815
摘要： A method and apparatus are provided for adapting a language model to a task-specific domain. Under the method and apparatus, the relative frequency of n-grams in a small training set (i.e. task-specific training data set) and the relative frequency of n-grams in a large training set (i.e. out-of-domain training data set) are used to weight a distribution count of n-grams in the large training set. The weighted distributions are then used to form a modified language model by identifying probabilities for n-grams from the weighted distributions.

7. 发明授权

US07430566B2 Statistical bigram correlation model for image retrieval 有权
标题翻译：图像检索统计二值相关模型
公开(公告)号：US07430566B2
公开(公告)日：2008-09-30
申请号：US11056555
申请日：2005-02-11
申请人： Mingjing Li , Zheng Chen , Liu Wenyin , Hong-Jiang Zhang
发明人： Mingjing Li , Zheng Chen , Liu Wenyin , Hong-Jiang Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30247 , Y10S707/99933 , Y10S707/99935 , Y10S707/99936 , Y10S707/99945 , Y10S707/99948
摘要： The disclosed subject matter improves iterative results of content-based image retrieval (CBIR) using a bigram model to correlate relevance feedback. Specifically, multiple images are received responsive to multiple image search sessions. Relevance feedback is used to determine whether the received images are semantically relevant. A respective semantic correlation between each of at least one pair of the images is then estimated using respective bigram frequencies. The bigram frequencies are based on multiple search sessions in which each image of a pair of images is semantically relevant.
摘要翻译：所公开的主题改进了基于内容的图像检索（CBIR）的迭代结果，其使用二元模型来相关联反馈。具体地，响应于多个图像搜索会话接收多个图像。相关性反馈用于确定接收的图像是否具有语义相关性。然后使用相应的二进制频率来估计至少一对图像中的每一个之间的相应语义相关性。双频频率基于多个搜索会话，其中一对图像的每个图像在语义上相关。

8. 发明申请

US20050165763A1 Statistical bigram correlation model for image retrieval 有权
公开(公告)号：US20050165763A1
公开(公告)日：2005-07-28
申请号：US11056555
申请日：2005-02-11
申请人： Mingjing Li , Zheng Chen , Liu Wenyin , Hong-Jiang Zhang
发明人： Mingjing Li , Zheng Chen , Liu Wenyin , Hong-Jiang Zhang
IPC分类号： G06F17/30 , G06F7/00
CPC分类号： G06F17/30247 , Y10S707/99933 , Y10S707/99935 , Y10S707/99936 , Y10S707/99945 , Y10S707/99948
摘要： The disclosed subject matter improves iterative results of content-based image retrieval (CBIR) using a bigram model to correlate relevance feedback. Specifically, multiple images are received responsive to multiple image search sessions. Relevance feedback is used to determine whether the received images are semantically relevant. A respective semantic correlation between each of at least one pair of the images is then estimated using respective bigram frequencies. The bigram frequencies are based on multiple search sessions in which each image of a pair of images is semantically relevant.

9. 发明授权

US06901411B2 Statistical bigram correlation model for image retrieval 失效
标题翻译：图像检索统计二值相关模型
公开(公告)号：US06901411B2
公开(公告)日：2005-05-31
申请号：US10074941
申请日：2002-02-11
申请人： Mingjing Li , Zheng Chen , Liu Wenyin , Hong-Jiang Zhang
发明人： Mingjing Li , Zheng Chen , Liu Wenyin , Hong-Jiang Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30247 , Y10S707/99933 , Y10S707/99935 , Y10S707/99936 , Y10S707/99945 , Y10S707/99948
摘要： The disclosed subject matter improves iterative results of content-based image retrieval (CBIR) using a bigram model to correlate relevance feedback. Specifically, multiple images are received responsive to multiple image search sessions. Relevance feedback is used to determine whether the received images are semantically relevant. A respective semantic correlation between each of at least one pair of the images is then estimated using respective bigram frequencies. The bigram frequencies are based on multiple search sessions in which each image of a pair of images is semantically relevant.
摘要翻译：所公开的主题改进了基于内容的图像检索（CBIR）的迭代结果，其使用二元模型来相关联反馈。具体地，响应于多个图像搜索会话接收多个图像。相关性反馈用于确定接收的图像是否具有语义相关性。然后使用相应的二进制频率来估计至少一对图像中的每一个之间的相应语义相关性。双频频率基于多个搜索会话，其中一对图像的每个图像在语义上相关。

10. 发明授权

US08594468B2 Statistical approach to large-scale image annotation 有权
标题翻译：大规模图像注释的统计方法
公开(公告)号：US08594468B2
公开(公告)日：2013-11-26
申请号：US13406804
申请日：2012-02-28
申请人： Mingjing Li , Xiaoguang Rui
发明人： Mingjing Li , Xiaoguang Rui
IPC分类号： G06K9/60
CPC分类号： G06K9/00684 , G06K2209/27
摘要： Statistical approaches to large-scale image annotation are described. Generally, the annotation technique includes compiling visual features and textual information from a number of images, hashing the images visual features, and clustering the images based on their hash values. An example system builds statistical language models from the clustered images and annotates the image by applying one of the statistical language models.
摘要翻译：描述了大规模图像注释的统计方法。通常，注释技术包括从许多图像编译视觉特征和文本信息，对图像进行散列视觉特征，并且基于它们的散列值对图像进行聚类。示例系统从群集图像构建统计语言模型，并通过应用统计语言模型之一来注释图像。

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