The field of intelligence has had its Jekyll and Hyde sides for me personally, which is why I entered the field in the first place. I became interested in intelligence when, as an elementary-school student, I did poorly on IQ tests. In fact, I did so poorly that in sixth grade I was sent back to a fifth-grade classroom to retake the fifth-grade intelligence test. In a sense, my professional career has been an attempt to understand and come to terms with my own early failures on these tests!
Handcock, M.S., Raftery, A.E. and Tantrum, J. (2005).
Model-Based Clustering for Social Networks.
Working Paper no. 46, Center for Statistics and the Social Sciences,
University of Washington.
«Traditionally, unification grammars are hand-coded. This is extremely time consuming, expensive and very difficult to scale. [...] we have developed a new method for automatically extracting wide-coverage probabilistic unification (LFG) grammars from treebank resources. To achieve this, we first automatically annotate the treebank (such as Penn-II) with feature-structure information (LFG f-structures, approximating to basic predicate-argument structure). From the f-structure annotated treebank, we then automatically extract wide-coverage, probabilistic LFG approximations to parse new text»
Herwig, J., Kittenberger, A., Nentwich, M. und Schmirmund, J., 2009, Microblogging und die Wissenschaft. Das Beispiel Twitter. Steckbrief 4 im Rahmen des Projekts "Interactive Science". ITA-Reports, Nr. a52-4 hrsg. v. Institut für Technikfolgen-Abschätzung, Wien: ITA
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