Description: Abstract: This paper describes experiments in metadata generation for learning object repositories. Specifically, the authors present several methods for automatic keyword extraction and evaluate them on a collection of learning objects from an undergraduate history course. The results suggest that automatic keyword extraction is a viable solution for suggesting terms and phrases for metadata annotation.

Description: This paper discusses BABYLON parallel text builder. Abstract: This paper describes BABYLON, a system that attempts to overcome the shortage of parallel texts in low-density languages by supplementing existing parallel texts with texts gathered automatically from the Web. In addition to the identification of entire Web pages, the authors also propose a new feature specifically designed to find parallel text chunks within a single document. Experiments carried out on the Quechua-Spanish language pair show that the system is successful in automatically identifying a significant amount of parallel texts on the Web. Evaluations of a machine translation system trained on this corpus indicate that the Web-gathered parallel texts can supplement manually compiled parallel texts and perform significantly better than the manually compiled texts when tested on other Web-gathered data.

Description: This paper discusses Open Mind Word Expert, an implemented active learning system for collecting word sense tagging from the general public over the Web. It is available at http://teach-computers.org. The authors expect the system to yield a large volume of high-quality training data at a much lower cost than the traditional method of hiring lexicographers. The authors thus propose a Senseval-3 lexical sample activity where the training data is collected via Open Mind Word Expert. If successful, the collection process can be extended to create the definitive corpus of word sense information.

Description: This presentation discusses call admission control (CAC). The authors define a set of feasible call configurations that results in a CAC algorithm that captures the effect of having an arbitrary traffic distribution and whose complexity scales linearly with the number of cells.

Description: This paper discusses capacity allocation in multi-cell UMTS networks. Abstract: An analytical model for calculating capacity in multi-cell UMTS networks is presented. Capacity is maximized for different spreading factors and for perfect and imperfect power control. An analytical model is presented for approximating the user distributions in multi-cell third generation WCDMA networks using 2-dimensional Gaussian distributions by determining the means and the standard deviations of the distribution for every cell. This allows for the calculation of the inter-cell interference and the reverse-link capacity of the network. The capacity was determined for signal-to-interference threshold from 5 dB to 10 dB and spreading factor values of 256, 64, 16, and 4.