Search queries are typically very short, which means they are often underspecified or have senses that the user did not think of. A broad latent query aspect is a set of keywords that succinctly represents one particular sense, or one particular information need, that can aid users in reformulating

pruning strategy when computing sparse, complete CUBES. We present a thorough performance evaluation over a broad range of workloads. Our evaluation demonstrates that (in contrast to earlier assumptions) minimizing the aggregations or the number of sorts is not the most important aspect of the sparse CUBE

social annotations and web queries; 2) SocialPageRank (SPR) captures the popularity of web pages. Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality (popularity) of a web page from the web

A latent-variable study examined whether verbal and visuospatial working memory (WM) capacity measures reflect a primarily domain-general construct by testing 236 participants in 3 span tests each of verbal WM, visuospatial WM, verbal short-term memory (STM), and visuospatial STM, as well

present a probabilistic framework based on PLSA to achieve the task of automatic image annotation and retrieval. The framework con-structs the latent space by using two PLSAmodels to capture seman-tic aspects fromvisual and textualmodalities respectively. Secondly, an adaptive asymmetric learning

Knowledge compilation has been emerging recently as a new direction of research for dealing with the computational intractability of general propositional reasoning. According to this approach, the reasoning process is split into two phases: an off-line compilation phase and an online query

the fundamental meaning for pragmatic aspects of such languages. We propose a basis for building systems satisfying these requirements. In this basis the role of an environment stack and naming-scoping-binding issues is central. Object-oriented database query/programming languages require modifications

We initiate the study of an interesting aspect of sponsored search advertising, namely the consequences of broad match- a feature where an ad of an advertiser can be mapped to a broader range of relevant queries, and not necessarily to the particular keyword(s) that ad is associated with. In spite

Approach we have tested in our submitted runs. Our approach consists in finding links between video shot key-frames, based on the use of a probabilistic latent space model over local matches between the keyframe images. This allows the extraction of significant groups of local matching descriptors

. This is commonly referred to as the problem of information overload. So the main focus is to add a new dimension to Internet- Searching and that is to apply semantic aspects towards it. In precise words, “the search must be what user wish, not what he/she types”. This paper presents the Latent semantic analysis