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 the code division multiple access (CDMA) inter-cell effects, capacity regions, maximizing network capacity, mobility, a call admission control algorithm, and network performance.

Description: This paper discusses dynamic agent population in agent-based distance vector routing. Abstract: The Intelligent mobile agent paradigm can be applied to a wide variety of intrinsically parallel and distributed applications. Network routing is one such application that can be mapped to an agent-based approach. The performance of any agent-based system will depend on its agent population. Although a lot of research has been conducted on agent-based systems, little consideration has been given to the importance of agent population in dynamic networks. A large number of constituent agents can increase the resource overhead of the system, thereby impeding the overall performance of the network. Hence, it is imperative to find the optimal number of agents in the system that would maximize the efficiency of the agent-based mechanism in the network. This optimal value cannot be determined manually, thereby emphasizing the need for an adaptive approach that manipulates the number of agents in the system based on its resource availability. This paper discusses an agent-based approach to Distance Vector Routing, referred as Agent-based Distance Vector Routing and also describes an adaptive approach controlling the number of agents in the network using pheromones and discusses their limitations.

Description: This paper discusses instance based learning with automatic feature selection applied to word sense disambiguation. Abstract We describe an algorithm for Word Sense Disambiguation (WSD) that relies on a lazy learner improved with automatic feature selection. The algorithm was implemented in a system that achieves excellent performance on the set of data released during the SENSEVAL-2 competition. We present the results obtained and discuss the performance of various features in the context of supervised learning algorithms for WSD.

Description: This article discusses Open Mind Word Expert (OMWE). The World Wide Web has both exacerbated the need and provided an opportunity for creating automatic tools for language processing. To tap the full potential of the Web, we need accurate information extraction, summarization and navigation technologies. None of these can come close to human-level performance without advancing the state of the art on how machines "make sense" of the text they are to process. One notoriously difficult problem in understanding text has been word sense disambiguation (WSD). Ambiguity is very common (especially among the most common words - think about "table", or "computer fan"), but people are so good at figuring it out from context that usually they do not even notice it. OMWE is a system that aims to tap people's ability to disambiguate words and to give computers the benefit of people's knowledge. Any Web user can visit the OMWE site and contribute some knowledge about the meanings of given words in given sentences. As a result, OMWE creates large sense-tagged corpora that can be used to build automatic WSD systems.