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Table 3 describes the overall performance of the baseline and the supervised system over a gold standard dataset. We randomly sampled 500 sentences containing at least one occurrence of our use case LU set from the input corpus. We first outsourced the annotation to the crowd as per the training set construction and the results were further manually validated twice by the authors. Measures are computed as follows: (1) a true positive is triggered if the predicted label is correct and the predicted text chunk at least partially matches the expected one; (2) chunks that should not be labeled are marked with a “O” and not explicitly counted as true or false positives.
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