Description: Abstract: This paper describes an algorithm for open text shallow semantic parsing. The algorithm relies on a frame dataset (FrameNet) and a semantic network (WordNet), to identify semantic relations between words in open text, as well as shallow semantic features associated with concepts in the text. Parsing semantic structures allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.

Description: This paper discusses an evaluation exercise for Romanian word sense disambiguation. Abstract: This paper presents the task definition, resources, participating systems, and comparative results for a Romanian Word Sense Disambiguation task, which was organized as part of the SENSEVAL-3 evaluation exercise. Five teams with a total of seven systems were drawn to this task.

Description: This paper discusses global versus local call admission control. Abstract: We design and implement global and local CAC algorithms for CDMA networks, and compare their network throughput for various mobility scenarios. The global CAC algorithms is inherently optimized and uses global information in making every call admission decision; it yields the best possible performance but has an intensive computational complexity. The design of the local CAC algorithm uses global information but its implementation in each cell uses only local information; it only requires the number of calls currently active in that cell and thus is very simple to implement. We show that our optimized local CAC algorithm achieves almost the same performance as our global CAC algorithm for a given call arrival rate profile.

Description: This paper discusses an impact of interference model on capacity in CDMA cellular networks. Abstract: An overwhelming number of models in the literature use average interference for calculation of capacity of a CDMA network. In this paper, we calculate the actual per-user interference and analyze the effect of user-distribution on the capacity of a CDMA network. We show that even though the capacity obtained using average interference is a good approximation to the capacity calculated using actual interference for a uniform user distribution, the deviation can be tremendously large for non-uniform user distributions.

Description: This paper discusses the main lines of research in deriving efficient Word Sense Disambiguation. 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.