Description: This paper discusses automatic recognition of humor. Abstract: Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this paper, the authors bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, the authors show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines.

Description: This presentation discusses wireless networks, access point selections, traffic balancing, multi-cell CDMA, user distribution modeling, and call admission control.

Description: This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.

Description: This paper discusses automatic generating of back-of-the-book indexes. Abstract: The automatic generation of back-of-the-book indexes seems to be out of sight of the Information Retrieval and Natural Language Processing communities, although the increasingly large number of books available in electronic format, as well as recent advances in key-phrase extraction, should motivate an increased interest in this topic. In this paper, the authors describe the background relevant to the process of creating back-of-the-book indexes, namely (1) a short overview of the origin and structure of back-of-the-book indexes, and (2) the correspondence that can be established between techniques for automatic index construction and keyphrase extraction. Since the development of any automatic system requires in the first place an evaluation testbed, the authors describe their work in building a gold standard collection of books and indexes, and the authors present several metrics that can be used for the evaluation of automatically generated indexes against the gold standard. Finally, the authors investigate the properties of the gold standard index, such as index size, length of index entries, and upper bounds on coverage as indicated by the presence of index entries in the document.

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.