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1. 发明授权

US06904402B1 System and iterative method for lexicon, segmentation and language model joint optimization 有权
标题翻译：词法，分割和语言模型联合优化的系统迭代法
公开(公告)号：US06904402B1
公开(公告)日：2005-06-07
申请号：US09609202
申请日：2000-06-30
申请人： Hai-Feng Wang , Chang-Ning Huang , Kai-Fu Lee , Shuo Di , Jianfeng Gao , Dong-Feng Cai , Lee-Feng Chien
发明人： Hai-Feng Wang , Chang-Ning Huang , Kai-Fu Lee , Shuo Di , Jianfeng Gao , Dong-Feng Cai , Lee-Feng Chien
IPC分类号： G06F17/28 , G06F17/27 , G10L15/06 , G10L15/18 , G06F17/21 , G06F17/20 , G10L15/00
CPC分类号： G06F17/274 , G10L15/197
摘要： A method for optimizing a language model is presented comprising developing an initial language model from a lexicon and segmentation derived from a received corpus using a maximum match technique, and iteratively refining the initial language model by dynamically updating the lexicon and re-segmenting the corpus according to statistical principles until a threshold of predictive capability is achieved.
摘要翻译：提出了一种用于优化语言模型的方法，其包括使用最大匹配技术从词典和从接收到的语料库导出的分割开发初始语言模型，并且通过动态地更新词典并重新分割语料库来迭代地改进初始语言模型统计原理，直到达到预测能力的阈值。

2. 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。

3. 发明授权

US06766320B1 Search engine with natural language-based robust parsing for user query and relevance feedback learning 有权
标题翻译：搜索引擎采用基于自然语言的强大解析，用于用户查询和相关性反馈学习
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。

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