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- 07-18张瑾晖 方面级情感分析--基于Attention机制模型的演进
- 07-16朱晓旭 GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling
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近日, 收到期刊《Expert Systems with Application》编辑部邮件，实验室林原老师等的研究工作“FGFIREM: A Feature Generation Framework based on Information Retrieval Evaluation Measures”已被录用。
Abstract：Learning to rank has become one of the most popular research areas in recent years. A series of learning to rank algorithms have been proposed to improve the ranking performance. In this work, we propose three learning to rank algorithms by directly optimizing evaluation measures based on the AdaRank algorithms. We name the three algorithms as AdaRank-ERR , AdaRank-MRR and AdaRank-Q, which optimize three evaluation measures, Expected Reciprocal Rank (ERR), Mean Reciprocal Rank (MRR), and Q-measure (Q), based on AdaRank, respectively. Furthermore, we propose a novel feature generation framework FGFIREM to enhance the ranking performance. The framework generates effective document ranking features based on the ranking scores assigned by the proposed algorithms, and enriches the original feature space of learning to rank using the generated features for improving the ranking performance. We evaluate the proposed framework on three datasets from LETOR3.0 and the web dataset MSLR-WEB10K. The experimental results demonstrate that our framework can effectively improve the ranking performance.