Catálogo de publicaciones - libros

In this paper, we investigate how to combine multiple e-mail filters based on multivariate statistical analysis for providing a barrier to spam, which is stronger than a single filter alone. Three evaluation criteria are suggested for cost-sensitive filters, and their rationality is discussed. Furthermore, a principle that minimizes the error cost is described to avoid filtering an e-mail of “Legitimate” into “Spam”. Comparing with other major methods, the experimental results show that our method of combining multiple filters has preferable performance when appropriate running parameters are adopted.

The Human Resource departments are now facing a new challenge: how to contribute in the definition of incentive plans and professional development? The participation of the line managers in answering this question is fundamental, since they are those who best know the single individuals; but they do not have the necessary background. In this paper, we present the project, which goal is to enable the line managers to be in charge of their own development plans by providing them with a personalized and contextualized set of information about their teams. Several experiments are reported, together with a discussion of the results.

In this paper we describe how to mine association rules in temporal document collections. We describe how to perform the various steps in the temporal text mining process, including data cleaning, text refinement, temporal association rule mining and rule post-processing. We also describe the Temporal Text Mining Testbench, which is a user-friendly and versatile tool for performing temporal text mining, and some results from using this tool.

Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. SRES is a self-supervised Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target elations and their attributes. SRES automatically generates the training data needed for its pattern-learning component. We also compare the performance of SRES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than SRES.