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1. 发明申请

US20100153315A1 BOOSTING ALGORITHM FOR RANKING MODEL ADAPTATION 有权
标题翻译：用于排序模型适应的增强算法
公开(公告)号：US20100153315A1
公开(公告)日：2010-06-17
申请号：US12337623
申请日：2008-12-17
申请人： Jianfeng Gao , Yi Su , Qiang Wu , Chris J.C. Burges , Krysta Svore , Elbio Renato Torres Abib
发明人： Jianfeng Gao , Yi Su , Qiang Wu , Chris J.C. Burges , Krysta Svore , Elbio Renato Torres Abib
IPC分类号： G06F15/18 , G06F17/30
CPC分类号： G06F17/3053
摘要： Model adaptation may be performed to take a general model trained with a set of training data (possibly large), and adapt the model using a set of domain-specific training data (possibly small). The parameters, structure, or configuration of a model trained in one domain (called the background domain) may be adapted to a different domain (called the adaptation domain), for which there may be a limited amount of training data. The adaption may be performed using the Boosting Algorithm to select an optimal basis function that optimizes a measure of error of the model as it is being iteratively refined, i.e., adapted.
摘要翻译：可以执行模型适配以采用用一组训练数据（可能较大）训练的通用模型，并且使用一组特定领域的训练数据（可能小）来适配模型。在一个域（称为背景域）中训练的模型的参数，结构或配置可以适应于可能存在有限量的训练数据的不同域（称为适配域）。可以使用升压算法来执行自适应，以选择最优基函数，该优化基函数优化模型的误差量度，因为其被迭代地改进，即适应。

2. 发明申请

US20090106223A1 ENTERPRISE RELEVANCY RANKING USING A NEURAL NETWORK 有权
标题翻译：使用神经网络的企业相关排名
公开(公告)号：US20090106223A1
公开(公告)日：2009-04-23
申请号：US11874844
申请日：2007-10-18
申请人： Dmitriy Meyerzon , Yauhen Shnitko , Chris J.C. Burges , Michael James Taylor
发明人： Dmitriy Meyerzon , Yauhen Shnitko , Chris J.C. Burges , Michael James Taylor
IPC分类号： G06F7/00
CPC分类号： G06F17/30864 , G06N3/02
摘要： A neural network is used to process a set of ranking features in order to determine the relevancy ranking for a set of documents or other items. The neural network calculates a predicted relevancy score for each document and the documents can then be ordered by that score. Alternate embodiments apply a set of data transformations to the ranking features before they are input to the neural network. Training can be used to adapt both the neural network and certain of the data transformations to target environments.
摘要翻译：使用神经网络来处理一组排名特征，以便确定一组文档或其他项目的相关性排名。神经网络计算每个文档的预测相关性分数，然后可以通过该分数排序文档。替代实施例在排序特征被输入到神经网络之前将一组数据变换应用于排序特征。训练可以用来适应神经网络和某些数据转换到目标环境。

3. 发明申请

US20120330647A1 HIERARCHICAL MODELS FOR LANGUAGE MODELING 有权
标题翻译：语言建模分层模型
公开(公告)号：US20120330647A1
公开(公告)日：2012-12-27
申请号：US13167734
申请日：2011-06-24
申请人： Chris J.C. Burges , Andrzej Pastusiak
发明人： Chris J.C. Burges , Andrzej Pastusiak
IPC分类号： G06F17/27
CPC分类号： G06F17/277 , G06F17/2818
摘要： The described implementations relate to natural language processing, and more particularly to training a language prior model using a model structure. The language prior model can be trained using parameterized representations of lexical structures such as training sentences, as well as parameterized representations of lexical units such as words or n-grams. During training, the parameterized representations of the lexical structures and the lexical units can be adjusted using the model structure. When the language prior model is trained, the parameterized representations of the lexical structures can reflect how the lexical units were used in the lexical structures.
摘要翻译：所描述的实现涉及自然语言处理，更具体地涉及使用模型结构训练语言先验模型。语言先验模型可以使用诸如训练句子的词汇结构的参数化表示以及词汇单元（例如单词或n-gram）的参数化表示进行训练。在训练中，可以使用模型结构调整词汇结构和词汇单位的参数化表示。当语言先验模型被训练时，词汇结构的参数化表示可以反映词汇单元在词汇结构中的使用。

4. 发明申请

US20120158710A1 MULTI-TIERED INFORMATION RETRIEVAL TRAINING 审中-公开
标题翻译：多层次的信息检索培训
公开(公告)号：US20120158710A1
公开(公告)日：2012-06-21
申请号：US12974704
申请日：2010-12-21
申请人： Chris J.C. Burges , Krysta M. Svore , Maksims Volkovs
发明人： Chris J.C. Burges , Krysta M. Svore , Maksims Volkovs
IPC分类号： G06F17/30
CPC分类号： G06F16/3347 , G06F16/3326 , G06N20/00
摘要： Methods and systems for multi-tiered information retrieval training are disclosed. A method includes identifying results in a ranked ordering of results that can be swapped without changing a score determined using a first ranking quality measure, determining a first vector and at least one other vector for each identified swappable result in the ranked ordering of results based on the first ranking quality measure and at least one other ranking quality measure respectively, and adding the first vector and the at least one other vector for each identified swappable result in the ranked ordering of results to obtain a function of the first vector and the at least one other vector. Access is provided to the function of the first vector and the at least one other vector for use in the multi-tiered information retrieval training.
摘要翻译：披露了多层次信息检索培训的方法和系统。一种方法包括：在不改变使用第一排序质量度量确定的分数的情况下，将结果的排序顺序识别为结果排序的结果，确定第一向量和至少一个其他向量，以便根据排序的结果顺序，基于第一等级质量度量和至少一个其他排序质量度量，并且对于每个所识别的可交换结果，对结果的排序顺序添加第一向量和至少一个其他向量，以获得第一向量的功能，并且至少另一个向量。提供对第一向量和至少一个其他向量的功能的访问以用于多层次信息检索训练。

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