CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity
Table 1: Results of the methods on SemEval-2016 STS cross-lingual evaluation dataset.
['[BOLD] Methods', '[BOLD] News', '[BOLD] Multi', '[BOLD] Mean']
The dataset, the annotation and the evaluation systems were presented in the SemEval-2016 STS task description paper Agirre et al. The lines in bold represent the methods that obtain the best mean score in each category of system (best method alone, unsupervised and supervised fusion). The scores for the supervised systems are obtained with 10-folds cross-validation. Run 1: Best Method Alone. Run 3: M5′ Model Tree. Unlike the two precedent runs, the third run is a supervised system. (called M5P in Weka 3.8.0 Hall et al. Model trees have a conventional decision tree structure but use linear regression functions at the leaves instead of discrete class labels. The first implementation of model trees, M5, was proposed by \newcitem5 and the approach was refined and improved in a system called M5′ by \newcitem5p.