公开(公告)号：US06848080B1

公开(公告)日：2005-01-25

申请号：US09606660

申请日：2000-06-28

申请人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

发明人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

IPC分类号： G06F17/21 ,  G06F17/22 ,  G06F17/27 ,  G06F17/28 ,  G06F17/24

CPC分类号： G06F17/273 ,  G06F17/2223 ,  G06F17/2715 ,  G06F17/2818 ,  G06F17/2863

摘要： A language input architecture converts input strings of phonetic text to an output string of language text. The language input architecture has a search engine, one or more typing models, a language model, and one or more lexicons for different languages. The typing model is configured to generate a list of probable typing candidates that may be substituted for the input string based on probabilities of how likely each of the candidate strings was incorrectly entered as the input string. The language model provides probable conversion strings for each of the typing candidates based on probabilities of how likely a probable conversion output string represents the candidate string. The search engine combines the probabilities of the typing and language models to find the most probable conversion string that represents a converted form of the input string.

摘要翻译： 语言输入架构将语音文本的输入字符串转换为语言文本的输出字符串。 语言输入体系结构具有搜索引擎，一个或多个输入模型，语言模型以及用于不同语言的一个或多个词典。 打字模型被配置为基于每个候选字符串被错误地输入作为输入字符串的可能性的概率来生成可替代输入字符串的可能的输入候选的列表。 语言模型基于可能的转换输出字符串表示候选字符串的可能性的概率，为每个输入候选提供可能的转换字符串。 搜索引擎结合了打字和语言模型的概率，以找到表示输入字符串的转换形式的最可能的转换字符串。

公开(公告)号：US06766320B1

公开(公告)日：2004-07-20

申请号：US09645806

申请日：2000-08-24

申请人： Hai-Feng Wang ,  Kai-Fu Lee ,  Qiang Yang

发明人： Hai-Feng Wang ,  Kai-Fu Lee ,  Qiang Yang

IPC分类号： G06F1730

CPC分类号： G06F17/30616 ,  G06F17/3043 ,  G06F2216/03 ,  Y10S707/99933 ,  Y10S707/99935

摘要： A search engine architecture is designed to handle a full range of user queries, from complex sentence-based queries to simple keyword searches. The search engine architecture includes a natural language parser that parses a user query and extracts syntactic and semantic information. The parser is robust in the sense that it not only returns fully-parsed results (e.g., a parse tree), but is also capable of returning partially-parsed fragments in those cases where more accurate or descriptive information in the user query is unavailable. A question matcher is employed to match the fully-parsed output and the partially-parsed fragments to a set of frequently asked questions (FAQs) stored in a database. The question matcher then correlates the questions with a group of possible answers arranged in standard templates that represent possible solutions to the user query. The search engine architecture also has a keyword searcher to locate other possible answers by searching on any keywords returned from the parser. The answers returned from the question matcher and the keyword searcher are presented to the user for confirmation as to which answer best represents the user's intentions when entering the initial search query. The search engine architecture logs the queries, the answers returned to the user, and the user's confirmation feedback in a log database. The search engine has a log analyzer to evaluate the log database to glean information that improves performance of the search engine over time by training the parser and the question matcher.

摘要翻译： 搜索引擎架构旨在处理从复杂的基于句子的查询到简单关键词搜索的全面的用户查询。 搜索引擎架构包括解析用户查询并提取句法和语义信息的自然语言解析器。 解析器在其不仅返回完全解析的结果（例如解析树）的意义上是鲁棒的，而且还能够在用户查询中更准确或描述性的信息不可用的情况下返回部分解析的片段。 使用问题匹配器将完全解析的输出和部分解析的片段与存储在数据库中的一组常见问题（FAQ）进行匹配。 然后，问题匹配器将问题与标准模板中排列的一组可能的答案相关联，这些答案代表用户查询的可能解决方案。 搜索引擎架构还具有一个关键字搜索器，通过搜索解析器返回的任何关键字来定位其他可能的答案。 从问题匹配器和关键词搜索器返回的答案被呈现给用户以确认哪个答案最好地表示用户在输入初始搜索查询时的意图。 搜索引擎架构将查询记录，返回给用户的答案以及用户在日志数据库中的确认反馈记录。 搜索引擎有一个日志分析器来评估日志数据库以收集信息，通过训练解析器和问题匹配器来提高搜索引擎的性能。

公开(公告)号：US5577135A

公开(公告)日：1996-11-19

申请号：US204031

申请日：1994-03-01

申请人： Kamil A. Grajski ,  Yen-Lu Chow ,  Kai-Fu Lee

发明人： Kamil A. Grajski ,  Yen-Lu Chow ,  Kai-Fu Lee

IPC分类号： G06K9/22 ,  G06K9/62 ,  G06K9/36 ,  G06K9/00

CPC分类号： G06K9/00422 ,  G06K9/6218

摘要： A handwriting signal processing front-end method and apparatus for a handwriting training and recognition system which includes non-uniform segmentation and feature extraction in combination with multiple vector quantization. In a training phase, digitized handwriting samples are partitioned into segments of unequal length. Features are extracted from the segments and are grouped to form feature vectors for each segment. Groups of adjacent from feature vectors are then combined to form input frames. Feature-specific vectors are formed by grouping features of the same type from each of the feature vectors within a frame. Multiple vector quantization is then performed on each feature-specific vector to statistically model the distributions of the vectors for each feature by identifying clusters of the vectors and determining the mean locations of the vectors in the clusters. Each mean location is represented by a codebook symbol and this information is stored in a codebook for each feature. These codebooks are then used to train a recognition system. In the testing phase, where the recognition system is to identify handwriting, digitized test handwriting is first processed as in the training phase to generate feature-specific vectors from input frames. Multiple vector quantization is then performed on each feature-specific vector to represent the feature-specific vector using the codebook symbols that were generated for that feature during training. The resulting series of codebook symbols effects a reduced representation of the sampled handwriting data and is used for subsequent handwriting recognition.

摘要翻译： 一种用于手写训练和识别系统的手写信号处理前端方法和装置，其包括与多个矢量量化相结合的非均匀分割和特征提取。 在训练阶段，数字化手写样本被划分成不等长的段。 从段中提取特征，并将其分组以形成每个段的特征向量。 然后组合来自特征向量的相邻组以形成输入帧。 特征向量通过从帧内的每个特征向量分组相同类型的特征来形成。 然后对每个特征向量执行多向量量化，以通过识别向量的簇并确定簇中的向量的平均位置来统计地对每个特征的向量的分布进行建模。 每个平均位置由码本符号表示，并且该信息存储在每个特征的码本中。 然后将这些码本用于训练识别系统。 在识别系统识别笔迹的测试阶段，数字化测试笔迹首先在训练阶段进行处理，以从输入框中生成特征向量。 然后对每个特征向量执行多向量量化，以使用在训练期间为该特征生成的码本符号来表示特征向量。 所得到的一系列码本符号影响了采样笔迹数据的缩小表示，并被用于随后的手写识别。

公开(公告)号：US07424675B2

公开(公告)日：2008-09-09

申请号：US10951307

申请日：2004-09-27

申请人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

发明人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

IPC分类号： G06F17/00

CPC分类号： G06F17/273 ,  G06F17/2223 ,  G06F17/2715 ,  G06F17/2818 ,  G06F17/2863

摘要： A language input architecture converts input strings of phonetic text to an output string of language text. The language input architecture has a search engine, one or more typing models, a language model, and one or more lexicons for different languages. The typing model is configured to generate a list of probable typing candidates that may be substituted for the input string based on probabilities of how likely each of the candidate strings was incorrectly entered as the input string. The language model provides probable conversion strings for each of the typing candidates based on probabilities of how likely a probable conversion output string represents the candidate string. The search engine combines the probabilities of the typing and language models to find the most probable conversion string that represents a converted form of the input string.

摘要翻译： 语言输入架构将语音文本的输入字符串转换为语言文本的输出字符串。 语言输入体系结构具有搜索引擎，一个或多个输入模型，语言模型以及用于不同语言的一个或多个词典。 打字模型被配置为基于每个候选字符串被错误地输入作为输入字符串的可能性的概率来生成可替代输入字符串的可能的输入候选的列表。 语言模型基于可能的转换输出字符串表示候选字符串的可能性的概率，为每个输入候选提供可能的转换字符串。 搜索引擎结合了打字和语言模型的概率，以找到表示输入字符串的转换形式的最可能的转换字符串。

公开(公告)号：US20050086590A1

公开(公告)日：2005-04-21

申请号：US10970438

申请日：2004-10-21

申请人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

发明人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

IPC分类号： G06F17/21 ,  G06F17/22 ,  G06F17/27 ,  G06F17/28 ,  G06F15/00

CPC分类号： G06F17/273 ,  G06F17/2223 ,  G06F17/2715 ,  G06F17/2818 ,  G06F17/2863

摘要： A language input architecture converts input strings of phonetic text (e.g., Chinese Pinyin) to an output string of language text (e.g., Chinese Hanzi) in a manner that minimizes typographical errors and conversion errors that occur during conversion from the phonetic text to the language text. The language input architecture has a search engine, one or more typing models, a language model, and one or more lexicons for different languages. Each typing model is trained on real data, and learns probabilities of typing errors. The typing model is configured to generate a list of probable typing candidates that may be substituted for the input string based on probabilities of how likely each of the candidate strings was incorrectly entered as the input string. The probable typing candidates may be stored in a database. The language model provides probable conversion strings for each of the typing candidates based on probabilities of how likely a probable conversion output string represents the candidate string. The search engine combines the probabilities of the typing and language models to find the most probable conversion string that represents a converted form of the input string. By generating typing candidates and then using the associated conversion strings to replace the input string, the architecture eliminates many common typographical errors. When multiple typing models are employed, the architecture can automatically distinguish among multiple languages without requiring mode switching for entry of the different languages.

摘要翻译： 语言输入架构将语音文本的输入字符串（例如中文拼音）转换为语言文本的输出字符串（例如汉字），以最小化从语音文本转换为语言的排版错误和转换错误 文本。 语言输入体系结构具有搜索引擎，一个或多个输入模型，语言模型以及用于不同语言的一个或多个词典。 每个打字模型都对真实数据进行培训，并学习打字错误的可能性。 打字模型被配置为基于每个候选字符串被错误地输入作为输入字符串的可能性的概率来生成可替代输入字符串的可能的输入候选的列表。 可能的打字候选人可以存储在数据库中。 语言模型基于可能的转换输出字符串表示候选字符串的可能性的概率，为每个输入候选提供可能的转换字符串。 搜索引擎结合了打字和语言模型的概率，以找到表示输入字符串的转换形式的最可能的转换字符串。 通过生成打字候选人，然后使用关联的转换字符串来替换输入字符串，该架构消除了许多常见的打印错误。 当采用多种类型模型时，架构可以自动区分多种语言，而不需要为不同语言的输入进行模式切换。

公开(公告)号：US20050044495A1

公开(公告)日：2005-02-24

申请号：US10951307

申请日：2004-09-27

申请人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

发明人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

IPC分类号： G06F17/21 ,  G06F17/22 ,  G06F17/27 ,  G06F17/28 ,  G06F15/00

CPC分类号： G06F17/273 ,  G06F17/2223 ,  G06F17/2715 ,  G06F17/2818 ,  G06F17/2863

摘要： A language input architecture converts input strings of phonetic text to an output string of language text. The language input architecture has a search engine, one or more typing models, a language model, and one or more lexicons for different languages. The typing model is configured to generate a list of probable typing candidates that may be substituted for the input string based on probabilities of how likely each of the candidate strings was incorrectly entered as the input string. The language model provides probable conversion strings for each of the typing candidates based on probabilities of how likely a probable conversion output string represents the candidate string. The search engine combines the probabilities of the typing and language models to find the most probable conversion string that represents a converted form of the input string.

摘要翻译： 语言输入架构将语音文本的输入字符串转换为语言文本的输出字符串。 语言输入体系结构具有搜索引擎，一个或多个输入模型，语言模型以及用于不同语言的一个或多个词典。 打字模型被配置为基于每个候选字符串被错误地输入作为输入字符串的可能性的概率来生成可替代输入字符串的可能的输入候选的列表。 语言模型基于可能的转换输出字符串表示候选字符串的可能性的概率，为每个输入候选提供可能的转换字符串。 搜索引擎结合了打字和语言模型的概率，以找到表示输入字符串的转换形式的最可能的转换字符串。

公开(公告)号：US5734791A

公开(公告)日：1998-03-31

申请号：US999354

申请日：1992-12-31

申请人： Alejandro Acero ,  Kai-Fu Lee ,  Yen-Lu Chow

发明人： Alejandro Acero ,  Kai-Fu Lee ,  Yen-Lu Chow

IPC分类号： G10L19/02 ,  G10L3/02

CPC分类号： G10L19/038

摘要： The branching decision for each node in a vector quantization (VQ) binary tree is made by a simple comparison of a pre-selected element of the candidate vector with a stored threshold resulting in a binary decision for reaching the next lower level. Each node has a preassigned element and threshold value. Conventional centroid distance training techniques (such as LBG and k-means) are used to establish code-book indices corresponding to a set of VQ centroids. The set of training vectors are used a second time to select a vector element and threshold value at each node that approximately splits the data evenly. After processing the training vectors through the binary tree using threshold decisions, a histogram is generated for each code-book index that represents the number of times a training vector belonging to a given index set appeared at each index. The final quantization is accomplished by processing and then selecting the nearest centroid belonging to that histogram. Accuracy comparable to that achieved by conventional binary tree VQ is realized but with almost a full magnitude increase in processing speed.

摘要翻译： 矢量量化（VQ）二叉树中的每个节点的分支决定是通过将​​候选矢量的预先选择的元素与存储的阈值进行简单比较而得到的，从而产生用于达到下一较低级别的二进制决定。 每个节点具有预分配的元素和阈值。 传统的质心距离训练技术（如LBG和k-means）用于建立与一组VQ质心相对应的代码簿索引。 训练矢量集合被用于第二次在每个节点选择一个向量元素和阈值，每个节点大致分割数据。 在通过使用阈值判定的二进制树处理训练向量之后，针对代表每个索引处出现的给定索引集的训练向量的次数的每个代码簿索引生成直方图。 最后量化通过处理然后选择属于该直方图的最近质心来实现。 实现与常规二叉树VQ实现的精度相当的精度，但处理速度几乎提高了一个全面的幅度。

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。

公开(公告)号：US07165019B1

公开(公告)日：2007-01-16

申请号：US09606807

申请日：2000-06-28

申请人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

发明人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

IPC分类号： G06F17/28

CPC分类号： G06F17/273 ,  G06F17/2715 ,  G06F17/2775 ,  G06F17/2863

摘要： A language input architecture converts input strings of phonetic text (e.g., Chinese Pinyin) to an output string of language text (e.g., Chinese Hanzi) in a manner that minimizes typographical errors and conversion errors that occur during conversion from the phonetic text to the language text. The language input architecture has a search engine, one or more typing models, a language model, and one or more lexicons for different languages. Each typing model is trained on real data, and learns probabilities of typing errors. The typing model is configured to generate a list of probable typing candidates that may be substituted for the input string based on probabilities of how likely each of the candidate strings was incorrectly entered as the input string.

摘要翻译： 语言输入架构将语音文本的输入字符串（例如中文拼音）转换为语言文本的输出字符串（例如汉字），以最小化从语音文本转换为语言的排版错误和转换错误 文本。 语言输入体系结构具有搜索引擎，一个或多个输入模型，语言模型以及用于不同语言的一个或多个词典。 每个打字模型都对真实数据进行培训，并学习打字错误的可能性。 打字模型被配置为基于每个候选字符串被错误地输入作为输入字符串的可能性的概率来生成可替代输入字符串的可能的输入候选的列表。

公开(公告)号：US07302640B2

公开(公告)日：2007-11-27

申请号：US10970438

申请日：2004-10-21

申请人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

发明人： Kai-Fu Lee ,  Zheng Chen ,  Jian Han

IPC分类号： G06F15/00

CPC分类号： G06F17/273 ,  G06F17/2223 ,  G06F17/2715 ,  G06F17/2818 ,  G06F17/2863

摘要： A language input architecture converts input strings of phonetic text to an output string of language text. The language input architecture has a search engine, typing models, a language model, and one or more lexicons for different languages. Each typing model is trained on real data, and learns probabilities of typing errors. The typing model is configured to generate a list of probable typing candidates that may be substituted for the input string based on probabilities of how likely each of the candidate strings was incorrectly entered as the input string. The language model provides probable conversion strings for each of the typing candidates based on probabilities of how likely a probable conversion output string represents the candidate string. The search engine combines the probabilities of the typing and language models to find the most probable conversion string that represents a converted form of the input string.

摘要翻译： 语言输入架构将语音文本的输入字符串转换为语言文本的输出字符串。 语言输入架构具有搜索引擎，打字模型，语言模型以及用于不同语言的一个或多个词典。 每个打字模型都对真实数据进行培训，并学习打字错误的可能性。 打字模型被配置为基于每个候选字符串被错误地输入作为输入字符串的可能性的概率来生成可替代输入字符串的可能的输入候选的列表。 语言模型基于可能的转换输出字符串表示候选字符串的可能性的概率，为每个输入候选提供可能的转换字符串。 搜索引擎结合了打字和语言模型的概率，以找到表示输入字符串的转换形式的最可能的转换字符串。