This book chapter presents two special algorithms, Mean Value Analysis and Convolution Algorithm, for the analysis of closed queuing networks, and an introduction to simulation techniques that are widely used in analyzing queuing systems in general.
This paper discusses grid-based coordinated routing in wireless sensor networks and compares the energy available in the network over time for different grid sizes. The authors explore the quality of service of wireless sensor networks, how the coordinator nodes are elected, and the size of the grid area that will minimize the total energy consumption and extend the lifetime of the network.
In this paper, the authors summarize the main theories of humor that emerged from philosophical and modern psychological research, and survey the past and present developments in the fields of theoretical and computational linguistics.
This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.
This paper describes a method for generating sense-tagged data using Wikipedia as a source of sense annotations. Through word sense disambiguation experiments, the authors show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers.
This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks.
This paper investigates the problem of automatic humor recognition, and provides an in-depth analysis of two of the most frequently observed features of humorous text: human-centeredness and negative polarity. Through experiments performed on two collections of humorous texts, the authors show that these properties of verbal humor are consisted across different data sets.
The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, the authors describe the data set used in the evaluation and the results obtained by the participating systems.