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1. 发明申请

WO2007143109A3 CONCEPT BASED CROSS MEDIA INDEXING AND RETRIEVAL OF SPEECH DOCUMENTS 审中-公开
公开(公告)号：WO2007143109A3
公开(公告)日：2007-12-13
申请号：PCT/US2007/012965
申请日：2007-06-01
申请人： TELCORDIA TECHNOLOGIES, INC. , BEHRENS, Clifford, A. , EGAN, Dennis , BASSU, Devasis
发明人： BEHRENS, Clifford, A. , EGAN, Dennis , BASSU, Devasis
IPC分类号： G06F17/30 , G01L21/00
摘要： Indexing, searching, and retrieving the content of speech documents (including but not limited to recorded books, audio broadcasts, recorded conversations) is accomplished by finding and retrieving speech documents that are related to a query term at a conceptual level, even if the speech documents does not contain the spoken (or textual) query terms. Concept-based cross-media information retrieval is used. A term-phoneme/document matrix is constructed from a training set of documents. Documents are then added to the matrix constructed from the training data. Singular Value Decomposition is used to compute a vector space from the term-phoneme/document matrix. The result is a lower-dimensional numerical space where term-phoneme and document vectors are related conceptually as nearest neighbors. A query engine computes a cosine value between the query vector and all other vectors in the space and returns a list of those term-phonemes and/or documents with the highest cosine value.

2. 发明申请

WO2004100130A2 INFORMATION RETRIEVAL AND TEXT MINING USING DISTRIBUTED LATENT SEMANTIC INDEXING 审中-公开
标题翻译：信息检索和文本挖掘使用分布式的专有语义索引
公开(公告)号：WO2004100130A2
公开(公告)日：2004-11-18
申请号：PCT/US2004/012462
申请日：2004-04-23
申请人： TELCORDIA TECHNOLOGIES, INC.
发明人： BEHRENS, Clifford, A. , BASSU, Devasis
IPC分类号： G11B
CPC分类号： G06F17/3071 , Y10S707/99935 , Y10S707/99943
摘要： The use of latent semantic indexing (LSI) for information retrieval and text mining operations is adapted to work on large heterogeneous data sets by first partitioning the data set into a number of smaller partitions having similar concept domains. A similarity graph network is generated in order to expose links between concept domains which are then exploited in determining which domains to query as well as in expanding the query vector. LSI is performed on those partitioned data sets most likely to contain information related to the user query or text mining operation. In this manner LSI can be applied to datasets that heretofore presented scalability problems. Additionally, the computation of the singular value decomposition of the term-by-document matrix can be accomplished at various distributed computers increasing the robustness of the retrieval and text mining system while decreasing search times.
摘要翻译：利用潜在语义索引（LSI）进行信息检索和文本挖掘操作适用于大型异构数据集，首先将数据集划分为具有类似概念域的多个较小分区。生成相似图网络以便公开概念域之间的链接，然后在确定要查询的域以及扩展查询向量时利用这些链接。对最可能包含与用户查询或文本挖掘操作相关的信息的那些分区数据集执行LSI。以这种方式，LSI可以应用于迄今为止呈现的可扩展性问题的数据集。另外，逐个文档矩阵的奇异值分解的计算可以在各种分布式计算机上实现，从而增加检索和文本挖掘系统的鲁棒性，同时减少搜索时间。

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