Abstract
The query error correction task is very important to improve user satisfaction and quality of query results. In traditional query error correction methods researchers mostly use a pipeline way to correct the error step by step. They rely heavily on manual annotation corpora. It is difficult to take into account the global effect. In this paper, we present a character-based end-to-end Sequence to Sequence (Seq2Seq) method with attention mechanism. It also incorporates the neural network language model trained on unlabeled corpora to solve the task of query correction. It can unify the modeling of different error types in query error correction and effectively overcome the shortcomings of traditional methods in query error correction tasks. Experiments show that this method can effectively capture the long-distance knowledge to correct errors, and through the Simple Recurrent Unit (SRU) it can be as good as Long Short-Term Memory (LSTM). However, there has been a significant improvement in processing time. This point is very important in query error correction tasks.
This work was supported by the National Natural Science Foundation of China (61672040) and the North China University of Technology Startup Fund.
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Duan, J., Ji, T., Wu, M., Wang, H. (2019). Query Error Correction Algorithm Based on Fusion Sequence to Sequence Model. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_2
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