In this article we present Supervised Semantic Indexing (SSI) which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word...
Bing Bai, Jason Weston, David Grangier, Ronan Coll...
Semantic indexing is a popular technique used to access and organize large amounts of unstructured text data. We describe an optimized implementation of semantic indexing and docu...
In this paper, we present the results of our work that seek to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This wo...
Encouraged by a significant improvement over LSI (latent semantic indexing) approach in textual information retrieval of the DLSI (differential latent semantic indexing) approach ...
Popular image retrieval schemes generally rely only on a single mode, (either low level visual features or embedded text) for searching in multimedia databases. Many popular image...
Semantic analysis of a document collection can be viewed as an unsupervised clustering of the constituent words and documents around hidden or latent concepts. This has shown to i...
The global growth in popularity of the World Wide Web has been enabled in part by the availability of browser based search tools which in turn have led to an increased demand for ...
Yi-Ming Chung, William M. Pottenger, Bruce R. Scha...
As part of the Illinois Digital Library Initiative (DLI) project we developed “scalable semantics” technologies. These statistical techniques enabled us to index large collect...
Yi-Ming Chung, Qin He, Kevin Powell, Bruce R. Scha...
A research prototype is presented for semantic indexing and retrieval in Information Retrieval. The prototype is motivated by a desire to provide a more efficient and effective in...
Abstract. Problem solving with experiences that are recorded in text form requires a mapping from text to structured cases, so that case comparison can provide informed feedback fo...
Nirmalie Wiratunga, Robert Lothian, Sutanu Chakrab...