Description: This paper discusses the main lines of research in deriving efficient Word Sense Disambiguation. Abstract: In the past few years, we have witnessed a tremendous growth of the World Wide Web, both in terms of number of Web pages accessible online - resulting in what represents today the largest publicly available corpus, and in terms of number of Web users - who now these two main dimensions - pages and users - has opened the doors to a realm of new approaches to data-hungry and knowledge-hungry language processing applications. Among these, Word Sense Disambiguation is one of the applications that has the potential of benefiting the most from the large amounts of Web-based data and from the availability of inexpensive Web user supervision. In this paper, the author discusses the main lines of research in deriving efficient Word Sense Disambiguation methods that exploit: (1) the Web as a corpus - which represents a view of the Web seen as an enormous collection of Web pages; and (2) the Web as collective mind - where the Web is regarded as a large group of Web users who can contribute their knowledge to the process of identifying word meanings.

Description: This paper discusses a logic programming framework for semantic interpretation with WordNet and PageRank. Abstract: This paper describes applications of Logic Programming to Natural Language processing in combination with graph-algorithms and statistical methods. Google's PageRank and similar fast-converging recursive graph algorithms have provided practical means to statistically rank vertices of large graphs like the World Wide Web. By combining a fast Java-based PageRank implementation with a Prolog base inferential layer, running on top of an optimized WordNet graph, the authors describe applications to word sense disambiguation and evaluate their accuracy in comparison with human annotated corpus data.

Description: This paper presents a new open text word sense disambiguation method that combines the use of logical interferences with PageRank-style algorithms applied on graphs extracted from natural language documents. The authors evaluate the accuracy of the proposed algorithm on several sense-annotated texts, and show that it consistently out-performs the accuracy of other previously proposed knowledge-based word sense disambiguation methods. The authors also explore and evaluate methods that combine several open-text word sense disambiguation algorithms.

Description: This paper discusses subscriber maximization in CDMA cellular networks. The author calculates the maximum number of subscribers in a CDMA cellular network for a given GoS requirement, QoS requirement, network topology, and user distribution profile. The author formulates a constrained optimization problem that maximizes the call arrival rates subject to upper bounds on the blocking probabilities and lower bounds on the bit energy to interference ratios. This paper presents examples for traditional and optimized network topologies with uniform and non-uniform user distribution profiles and different mobility scenarios.

Description: This presentation discusses interference model impacts on capacity, global call admission controls, local call admission controls, and the differences in global versus local call admission controls.