Building a bilingual dictionary for transfer in a machine translation system is conventionally done by hand and is very time-consuming. In order to overcome this bottleneck, we propose a new mechanism for lexical transfer, which is simple and suitable for learning from bilingual corpora. It exploits a vector-space model developed in information retrieval research. We present a preliminary resul...

The Vector Space Model (VSM) is probably the most widely used model for retrieving information from text collections (and recently from over other kinds of corpi). Assuming this model, we study the problem of finding the best query that ”names” (or describes) a given (unordered or ordered) set of objects. We formulate several variations of this problem and we provide methods and algorithms for ...

Many applications involving similarity search use the QBIC Euclidian distance to match two color histograms. To alleviate certain problems associated with this approach, which is based on a distance metric, in this paper, we propose a Color-Color Similarity Retrieval Approach to compute the similarities between images. This approach, based on the similarity matrix between feature vectors, leads...

Vast amounts of text are now available in machine-readable form and can be processed electronically. The vector space model of text processing has been widely used and has consistently produced superior retrieval results for the last thirty years. Traditionally , information retrieval research has concentrated on improving on-demand retrieval of useful textual information. Often a user has no p...

Using several simplifications of the vector-space model for text retrieval queries, the authors seek the optimal balance between processing efficiency and retrieval effectiveness as expressed in relevant document rankings. fficient and effective text retrieval techniques are critical in managing the increasing amount of textual information available in electronic form. Yet text retrieval is a d...