公开(公告)号：US07013262B2

公开(公告)日：2006-03-14

申请号：US10072974

申请日：2002-02-12

申请人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

发明人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

IPC分类号： G06F17/27 ,  G06F17/28

CPC分类号： G09B19/06 ,  G06F17/274

摘要： An accurate grammar analyzer based on a so-called POST (part-of-speech tagged) parser and a learners' model for use in automated language learning applications such as the template-based ICALL (intelligent computer assisted language learning) system.

公开(公告)号：US06735581B2

公开(公告)日：2004-05-11

申请号：US09849985

申请日：2001-05-08

申请人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

发明人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

IPC分类号： G06F944

CPC分类号： G06N7/023 ,  G06N3/0436

摘要： A method of automatically generating a multi-variable fuzzy inference system using a Fourier series expansion. Sample sets are decomposed into a cluster of sample sets associated with given input variables. Fuzzy rules and membership functions are computed individually for each variable by solving a single input multiple outputs fuzzy system extracted from the set cluster. The resulting fuzzy rules and membership functions are composed and integrated back into the fuzzy system appropriate for the original sample set with a minimal computational cost. In addition, an overall system error can be related to errors at each stage of decomposition and composition, enabling error bounds or accuracy thresholds for each stage to be specified and ensuring the final precision of the resulting fuzzy system on the original sample set.

摘要翻译： 一种使用傅里叶级数展开自动生成多变量模糊推理系统的方法。 样本集被分解为与给定输入变量相关联的一组样本集。 通过求解从集群提取的单输入多输出模糊系统，为每个变量单独计算模糊规则和隶属函数。 所得到的模糊规则和隶属函数被组合并集成回到适用于原始样本集的模糊系统中，具有最小的计算成本。 此外，整体系统错误可能与分解和组合的每个阶段的错误相关，使得要指定每个阶段的误差界限或精度阈值，并确保原始样本集上所得到的模糊系统的最终精度。

公开(公告)号：US06804637B1

公开(公告)日：2004-10-12

申请号：US09597270

申请日：2000-06-20

申请人： Naoyuki Tokuda ,  Hiroyuki Sasai

发明人： Naoyuki Tokuda ,  Hiroyuki Sasai

IPC分类号： G06F1728

CPC分类号： G06F17/2836

摘要： To retrieve an optimum template pattern in response to an input sentence, a set of templates is arranged in a plurality of template blocks containing an arbitrary number of sentence components, including grammatically correct and/or incorrect components. A score is assigned to every word in the set of templates according to its importance. The candidate template patterns and the input sentence are retrieved, the scores of the matched words are calculated, and the total of the scores of the entire paths are calculated. Optimum level comparison values are then calculated using the score of the matching words as the numerator and the total score as the denominator. The candidate template pattern having the largest optimum level comparison value among optimum level comparison values that provide the largest numerator, is selected as the optimum template pattern. The input sentence is then corrected using this optimum template pattern.

摘要翻译： 为了响应于输入句子来检索最佳模板模式，一组模板被布置在包含任意数量的句子分量的多个模板块中，包括语法上正确和/或不正确的组件。 根据其重要性，将分数分配给模板集中的每个单词。 检索候选模板模式和输入句子，计算匹配词的分数，并计算整个路径的得分总数。 然后使用匹配词的分数作为分子和总分数作为分母来计算最佳水平比较值。 选择提供最大分子的最佳电平比较值中具有最大最佳电平比较值的候选模板图案作为最佳模板图案。 然后使用该最佳模板模式校正输入句子。

公开(公告)号：US07509296B2

公开(公告)日：2009-03-24

申请号：US10550090

申请日：2004-03-22

申请人： Naoyuki Tokuda ,  Liang Chen

发明人： Naoyuki Tokuda ,  Liang Chen

IPC分类号： G06F17/00 ,  G06N5/00

CPC分类号： G09B19/06

摘要： A learning system uses the concept of template automaton. “Various expected examples of learners” consisting of “correct” answers and “incorrect” answers are collected and a representative NLP technique such as HCS (heaviest common character string) or LCS (longest common character string) algorithm is used as an effective error diagnosis engine in the language learning system. These examples embedded in the template are used for diagnostic analysis of the answers of the learners. This diagnosis is performed by selecting a path of the highest similarity with the input sentence of the learner among a plenty of candidate paths. Thus, it is possible to automatize and simplify the time-requiring authoring task used in the language-oriented intelligent learning system.

摘要翻译： 学习系统使用模板自动机的概念。 收集“正确”答案和“不正确”答案组成的“学习者的各种预期实例”，并使用代表性的NLP技术，如HCS（最重的通用字符串）或LCS（最长公用字符串）算法作为有效的错误诊断 引擎在语言学习系统中。 模板中嵌入的这些示例用于对学习者的答案进行诊断分析。 通过在大量候选路径中选择与学习者的输入句子最高相似度的路径来执行该诊断。 因此，可以自动化和简化在面向语言的智能学习系统中使用的时间需求创作任务。

公开(公告)号：US07024400B2

公开(公告)日：2006-04-04

申请号：US09849984

申请日：2001-05-08

申请人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

发明人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

IPC分类号： G06F17/00 ,  G06E1/00

CPC分类号： G06F17/271 ,  G06F17/30705 ,  G06F17/30707 ,  G06F17/3071

摘要： A computerized method for automatic document classification based on a combined use of the projection and the distance of the differential document vectors to the differential latent semantics index (DLSI) spaces. The method includes the setting up of a DLSI space-based classifier to be stored in computer storage and the use of such classifier by a computer to evaluate the possibility of a document belonging to a given cluster using a posteriori probability function and to classify the document in the cluster. The classifier is effective in operating on very large numbers of documents such as with document retrieval systems over a distributed computer network.

摘要翻译： 一种基于投影的组合使用和差分文档向量与差分潜在语义索引（DLSI）空间的距离的自动文档分类的计算机化方法。 该方法包括设置要存储在计算机存储器中的基于DLSI空间的分类器，以及由计算机使用这种分类器以使用后验概率函数评估属于给定簇的文档的可能性并对文档进行分类 在集群中。 分类器对于大量文档（例如通过分布式计算机网络上的文档检索系统）有效。

公开(公告)号：US06988063B2

公开(公告)日：2006-01-17

申请号：US10072973

申请日：2002-02-12

申请人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

发明人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

IPC分类号： G06F17/27

CPC分类号： G06F17/274

摘要： An accurate grammar analyzer that works effectively even with error-ridden sentences input by learners, based on a context-free probabilistic statistical POST (part-of-speech tagged) parser, for a template-automation-based computer-assisted language learning system. For any keyed-in sentence, the parser finds a closest correct sentence to the keyed-in sentence from among the embedded template paths exploiting a highest similarity value, and generates a grammar tree for the correct sentence where some ambiguous words are preassigned by expert language teachers. The system marks the errors under the leaves of the grammar tree by identifying the differences between the keyed-in sentence and the grammar tree of the correct sentence as errors committed by learners. By identifying most frequently recurring grammatical errors of each student, the system sets up a learner's model, providing a unique level of contingent remediation most appropriate to each learner involved.

摘要翻译： 基于模板自动化的计算机辅助语言学习系统，基于无上下文的概率统计POST（部分语音标记）解析器，准确的语法分析器可以有效地工作，甚至由学习者输入的错误语句。 对于任何键入句子，解析器从使用最高相似度值的嵌入式模板路径中找到与键入句子最接近的正确句子，并为正确的句子生成语法树，其中一些歧义词由专家语言预先分配 教师。 系统通过将正确句子的键入句和语法树之间的差异识别为学习者所犯的错误，来标记语法树叶下的错误。 通过确定每个学生最常见的语法错误，系统建立了一个学习者的模型，为每个学习者提供了最适合的或有补救措施。

公开(公告)号：US06598019B1

公开(公告)日：2003-07-22

申请号：US09597269

申请日：2000-06-20

申请人： Naoyuki Tokuda ,  Hiroyuki Sasai

发明人： Naoyuki Tokuda ,  Hiroyuki Sasai

IPC分类号： G10L1528

CPC分类号： G10L15/26

摘要： To improve the precision in correction of an input sentence by using a template pattern for model sentence. A plurality of template patterns for the model sentence are provided beforehand. Each of the template patterns is regarded as a plurality of templates of words/phrases based on expertise of language teachers with scores assigned to the words according to their importance. The scores and subsequently the input sentence are read and analyzed in comparison with each of the template patterns and the total of scores of matching words is calculated. A template pattern having the highest total score is selected as an optimum template pattern and the input sentence is corrected using the optimum template pattern. This method improves the likelihood that a template pattern containing a larger number of important words is selected as the optimum template pattern.

摘要翻译： 通过使用模型句子的模板模式来提高输入句的校正精度。 预先提供用于模型句子的多个模板模式。 每个模板模式被视为基于语言教师的专业知识的单词/短语的多个模板，其具有根据其重要性分配给单词的分数。 与每个模板模式相比较，分析和随后的输入句被读取和分析，并计算匹配词的总分数。 选择具有最高总分数的模板图案作为最佳模板图案，并且使用最佳模板图案校正输入句子。 该方法提高了包含更多数量的重要单词的模板模式被选择为最佳模板模式的可能性。

公开(公告)号：US07124073B2

公开(公告)日：2006-10-17

申请号：US10072953

申请日：2002-02-12

申请人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

发明人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

IPC分类号： G06F17/28

CPC分类号： G06F17/2765 ,  G06F17/2836

摘要： A new, more efficient memory translation algorithm facilitating the acquisition of a most appropriate translation in a target language from among those of nearly narrowed-down candidates of translation by separately applying the so-called dimension reducing functions of a template automaton and the LSI (latent semantic index) technique. Both the template automaton and the LSI principle play an important role in implementing an efficient process of narrowing down an efficient solution space from among the many example sentences of the databases in a target language by exploiting their respective unique search space reduction function. Once developed into a fully operational system, an expert editor rather than an expert translator can tune up the translation memory system, markedly widening the range of available experts who can utilize the system.

公开(公告)号：US20060154218A1

公开(公告)日：2006-07-13

申请号：US10550090

申请日：2004-03-22

申请人： Naoyuki Tokuda ,  Liang Chen

发明人： Naoyuki Tokuda ,  Liang Chen

IPC分类号： G09B5/00

CPC分类号： G09B19/06

摘要： A learning system uses the concept of template automaton. “Various expected examples of learners” consisting of “correct” answers and “incorrect” answers are collected and a representative NLP technique such as HCS (heaviest common character string) or LCS (longest common character string) algorithm is used as an effective error diagnosis engine in the language learning system. These examples embedded in the template are used for diagnostic analysis of the answers of the learners. This diagnosis is performed by selecting a path of the highest similarity with the input sentence of the learner among a plenty of candidate paths. Thus, it is possible to automatize and simplify the time-requiring authoring task used in the language-oriented intelligent learning system.

公开(公告)号：US06654740B2

公开(公告)日：2003-11-25

申请号：US09849986

申请日：2001-05-08

申请人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

发明人： Naoyuki Tokuda ,  Liang Chen ,  Hiroyuki Sasai

IPC分类号： G06F1730

CPC分类号： G06K9/6247 ,  G06F17/3061 ,  Y10S707/917 ,  Y10S707/955 ,  Y10S707/99935 ,  Y10S707/99936

摘要： A computer-based information search and retrieval system and method for retrieving textual digital objects that makes full use of the projections of the documents onto both the reduced document space characterized by the singular value decomposition-based latent semantic structure and its orthogonal space. The resulting system and method has increased robustness, improving the instability of the traditional keyword search engine due to synonymy and/or polysemy of a natural language, and therefore is particularly suitable for web document searching over a distributed computer network such as the Internet.

摘要翻译： 一种用于检索文本数字对象的计算机信息搜索和检索系统和方法，该文本数字对象充分利用文档的投影到以奇异值分解为基础的潜在语义结构及其正交空间为特征的缩减文档空间上。 所产生的系统和方法增加了鲁棒性，改善了由于自然语言的同义和/或多义性而导致的传统关键词搜索引擎的不稳定性，因此特别适用于诸如互联网的分布式计算机网络上的网络文档搜索。