In terms of implementation, we used Scikit-learn 5 toolkit (Pedregosa, Varoquaux et al., 2011) to do the classification and the parameter settings for three SVR models are shown in the following table, we chose these parameters by experiences, Clf-2 and Clf-3 used the same setting, and a better result may be achieved through fine tuning: 'rbf' 0.16 100 0.1 Table 3 Parameter settings of our three classifiers After the prediction of the similarity scores of sentences, we conducted a post-processing step to boost and correct results, we truncate at the extre-    Table 4 reported the results of our method on SemEval 2015 Task 2a, from which we can know that our method outperformed the winning system by a big margin on the headlines, but only slightly better on the images.
we can get vector representations 1 and 2 of the two sentences.