The most attractive form of future network content will be multi-media including speech information, and such speech information usually carries the core concepts for the content. As a result, the spoken documents associated with the multi-media content very possibly can serve as the key for retrieval and browsing. This paper presents a new approach of hierarchical topic organization and visual...

Latent Semantic Analyis (LSA) consists in the use of SVD-based dimensionality-reduction to reduce the high dimensionality of vector representations of documents, where the dimensions of the vectors correspond simply to word counts in the documents. We show that that there are two contending, inequivalent, formulations of LSA. The distinction between the two is not generally noted and while some...

Based on information retrieval model especially probabilistic latent semantic indexing (PLSI) model, we discuss methods for classification and clustering of a set of documents. A method for classification is presented and is demonstrated its good performance by applying to a set of benchmark documents with free format (text only). Then the classification method is modified to a clustering metho...

Latent Semantic Analysis (LSA) is a statistical, corpus-based text comparison mechanism that was originally developed for the task of information retrieval, but in recent years has produced remarkably human-like abilities in a variety of language tasks. LSA has taken the Test of English as a Foreign Language and performed as well as non-native English speakers who were successful college applic...

Previous neuroimaging studies (e.g., Green et al., 2010; Kmiecik & Morrison, 2013) suggest the neurocognitive processes responsible for verbal analogical reasoning vary with the semantic distance between source and target. In order to further investigate how semantic and relational similarity interacts during reasoning, we presented the A-, B-, C-, and D-terms of verbal analogies sequentially w...

At ICSLP'00, we introduced the concept of data-driven semantic inference, an approach to command and control which in principle allows for any word constructs in command/query formulation. Unconstrained word strings are mapped onto the relevant action through an automated classi cation relying on latent semantic analysis: as a result, it is no longer necessary for users to memorize the exact sy...

We examined a method for extracting the low frequency important single-word terms from domain specific text. Firstly, domain-relevant fragments were extracted from the text with the help of a dependency tree. Then the fragments were clustered and candidate terms were defined using the semantic classifier. The studies suggest that this approach allows extracting even terms with a single occurrence.

Latent Semantic Indexing (LSI) is an extension of the vector retrieval method (e.g., Salton & McGill, 1983) in which the dependencies between terms and between documents, in addition to the associations between terms and documents, are explicitly taken into account. This is done by simultaneously modeling all the association of terms and documents. We assume that there is some underlying or "la...