In word sense disambiguation, a system attempts to determine the sense of a word from contextual features. Major barriers to building a high-performing word sense disambiguation system include the difficulty of labeling data for this task and of predicting fine-grained sense distinctions. These issues stem partly from the fact that the task is being treated in isolation from possible uses of automatically disambiguated data. In this paper, we consider the related task of word translation, where we wish to determine the correct translation of a word from context. We can use parallel language corpora as a large supply of partially labeled data for this task. We present algorithms for solving the word translation problem and demonstrate a significant improvement over a baseline system. We then show that the word-translation system can be used to improve performance on a simplified machinetranslation task and can effectively and accurately prune the set of candidate translations for a word.
Web applications suffer from software and configuration faults that lower their availability. Recovering from failure is dominated by the time interval between when these faults appear and when they are detected by site operators. We introduce a set of tools that augment the ability of operators to perceive the presence of failure: an automatic anomaly detector scours HTTP access logs to find changes in user behavior that are indicative of site failures, and a visualizer helps operators rapidly detect and diagnose problems. Visualization addresses a key question of autonomic computing of how to win operators' confidence so that new tools will be embraced. Evaluation performed using HTTP logs from Ebates.com demonstrates that these tools can enhance the detection of failure as well as shorten detection time. Our approach is application-generic and can be applied to any Web application without the need for instrumentation.
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