Purpose:Existing researches of predicting queries with news intents have tried to extract the classification features from extemal knowledge bases,this paper tries to present how to apply features extracted from query logs for automatic identification of news queries without using any external resources.Design/methodology/approach:First,we manually labeled 1,220 news queries from Sogou.com.Based on the analysis of these queries,we then identified three features of news queries in terms of query content,time of query occurrence and user click behavior.Afterwards,we used 12 effective features proposed in literature as baseline and conducted experiments based on the support vector machine (SVM) classifier.Finally,we compared the impacts of the features used in this paper on the identification of news queries.Findings:Compared with baseline features,the F-score has been improved from 0.6414 to 0.8368 after the use of three newly-identified features,among which the burst point (bst)was the most effective while predicting news queries.In addition,query expression (qes) was more useful than query terms,and among the click behavior-based features,news URL was the most effective one.Research limitations:Analyses based on features extracted from query logs might lead to produce limited results.Instead of short queries,the segmentation tool used in this study has been more widely applied for long texts.Practical implications:The research will be helpful for general-purpose search engines to address search intents for news events.Originality/value:Our approach provides a new and different perspective in recognizing queries with news intent without such large news corpora as blogs or Twitter.
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