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This paper describes the approach of the THUIR team at the WSDM Cup 2023Pre-training for Web Search task. This task requires the participant to rankthe relevant documents for each query. We propose a new data pre-processingmethod and conduct pre-training and fine-tuning with the processed data.Moreover, we extract statistical, axiomatic, and semantic features to enhancethe ranking performance. After the feature extraction, diverse learning-to-rankmodels are employed to merge those features. The experimental results show thesuperiority of our proposal. We finally achieve second place in thiscompetition.

Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank