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National exercises for the evaluation of research activity by universities are becoming regular practice in ever more countries. These exercises have mainly been conducted through the application of peer-review methods. Bibliometrics has not been able to offer a valid large-scale alternative because of almost overwhelming difficulties in identifying the true author of each publication. We will address this problem by presenting a heuristic approach to author name disambiguation in bibliometric datasets for large-scale research assessments. The application proposed concerns the Italian university system, comprising 80 universities and a research staff of over 60,000 scientists. The key advantage of the proposed approach is the ease of implementation. The algorithms are of practical application and have considerably better scalability and expandability properties than state-of-the-art unsupervised approaches. Moreover, the performance in terms of precision and recall, which can be further improved, seems thoroughly adequate for the typical needs of large-scale bibliometric research assessments. [ABSTRACT FROM AUTHOR]

With the increasing number of digital documents, the ability to automatically classify those documents both efficiently and accurately is becoming more critical and difficult. One of the major problems in text classification is the high dimensionality of feature space. We present the ambiguity measure (AM) feature-selection algorithm, which selects the most unambiguous features from the feature set. Unambiguous features are those features whose presence in a document indicate a strong degree of confidence that a document belongs to only one specific category. We apply AM feature selection on a naïve Bayes text classifier. We favorably show the effectiveness of our approach in outperforming eight existing feature-selection methods, using five benchmark datasets with a statistical significance of at least 95% confidence. The support vector machine (SVM) text classifier is shown to perform consistently better than the naïve Bayes text classifier. The drawback, however, is the time complexity in training a model. We further explore the effect of using the AM feature-selection method on an SVM text classifier. Our results indicate that the training time for the SVM algorithm can be reduced by more than 50%, while still improving the accuracy of the text classifier. We favorably show the effectiveness of our approach by demonstrating that it statistically significantly (99% confidence) outperforms eight existing feature-selection methods using four standard benchmark datasets. [ABSTRACT FROM AUTHOR]