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Instead of asking the judges to resolve the disagreements, we decided to only include the data points on which the two judges at least agree on the existence of CST relationships (regardless of type). 53). This is an important decision based on our understanding of the underlying linguistic phenomenon, instead of technical inability of dealing with noisy data. Since the ability to determine the existence of any CST relationships is important for many potential applications, we want the model to be as clean as possible.
E. e. 3 Ordering Location Classes We investigate how each location class is likely to have antecedents using the corpus. 2, the possible antecedents in are “tyushin”, “saibai”, and “tochiotome”. In this case, since “tochiotome” is the antecedent, this location class has one antecedent, and three possible antecedents. These numbers are counted through the whole corpus, and the score of the location class is calculated by the above formula. We then sort these scores and obtain location class order for each case markers of zero pronouns.
For testing, the system was given 100 articles that have correct dependency structure. 4) using three parameters: search strategy, distance measure, and scoring. 4, ex-7 corresponds to our approach, ex-1 is similar to the approach suggested by , and ex-3 is similar to . 4. The accuracies (F-measure) are calculated by evaluating both detection and antecedent estimation of zero pronouns together. jp/~taku-ku/software/TinySVM/ 20 Daisuke Kawahara and Sadao Kurohashi Fig. 4. Configurations of our experiments.