Task 3a: Team YORKU
نویسندگان
چکیده
We used learning-to-rank methods for training ranking model. Due to the limited number of training queries, we split them and conducted 5-fold cross validation. We set the proportion of training and testing as 4 : 6. For the features used in learning the model, a total of 231 features that are from multiple information retrieval models with different parameter settings were adopted. For the baseline run, we used Random Forest method to train the models with 5-fold cross validation. Only the binary relevance information were taken into account while training the model. Five trained models from 5-fold cross validation were used on the testing data to predict scores, and then we used equal weights to linearly combine the results given by different models. For run #5, we used 8 learning to rank methods to train models separately and linearly combine them together, and the binary relevance judgment was used as well. For run #6 and #7, graded relevance were taken into consideration. The difference between run 6# and #7 is that for run #6, multiple learning-to-rank methods were used while for run #7, only Random Forest method was used. The best result of the four runs is achieved by run #5, which used multiple models combination based on binary relevance judgment.
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تاریخ انتشار 2014