A customised grammar framework for query classification

作者：

Highlights：

• Our approach outperforms previous ones in identifying different query types.

• Our approach has practical advantages related to the reduced number of features.

• RandomForest outperformed other algorithms with 99.6% accuracy.

摘要

•Our approach outperforms previous ones in identifying different query types.•Our approach has practical advantages related to the reduced number of features.•RandomForest outperformed other algorithms with 99.6% accuracy.

论文关键词：Natural language processing (NLP),Information retrieval,Text classification,Query classification,Machine learning

论文评审过程：Received 3 June 2018, Revised 17 April 2019, Accepted 5 June 2019, Available online 7 June 2019, Version of Record 14 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.010