Overview of BioCreative II gene mention recognition.

Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions

Combining Text and Heuristics for Cost-Sensitive Spam Filtering

Spam filtering is a text categorization task that shows especial features that make it interesting and difficult. First, the task has been performed traditionally using heuristics from the domain. Second, a cost model is required to avoid misclassification of legitimate messages. We present a comparative evaluation of several machine learning algorithms applied to spam filtering, considering the text of the messages and a set of heuristics for the task. Cost-oriented biasing and evaluation is performed. 1 Introduction Spam, or more properly Unsolicited Commercial E-mail (UCE), is an increasing threat to the viability of Internet E-mail and a danger to Internet commerce. UCE senders take away resources from users and service suppliers without compensation and without authorization. A variety of counter-measures to UCE have been proposed, from technical to regulatory (Cranor and LaMacchia, 1998). Among the technical ones, the use of filtering methods is popular and effective. UCE filt..