Exploring features for automatic identification of news queries through query logs

Purpose:Existing researches of predicting queries with news intents have tried to extract the classification features from external 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 to0.8368 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.

Identifying user intent through query refinements

Purpose:In this paper,we attempt to use query refinements to identify users' search intents and seek a method for intent clustering based on real world query data.Design/methodology/approach:An experiment has been conducted to analyze selected search sessions from the American Online(AOL) query logs with a two-stage approach.The first stage is to identify underlying intent by combining query co-occurrence information with query expression similarity.The work in the second stage is to cluster identified results by constructing query vectors through performing random walks on a Markov graph.Findings:Average correctness for identifying search intent is 0.74.Precision,recall,F-score values for intent clustering are 0.73,0.72 and 0.71,respectively.The results indicate that combining session co-occurrence information and query expression similarity can further filter noises and our clustering method is more suitable for sparse data.Research limitations:We use the time-out threshold(15-minutc) method to group queries in one session,but a user may have multiple search goals at the same time and the multi-task behavior of a user is hard to capture in a session defined based on time notions.Practical implications:This study provides insights into the ways of understanding users' search intents by analyzing their queries and refinements from a new perspective.The results will help search engine developers to identify user intents.Originality/value:We propose a new method to identify users' search intents by combining session co-occurrence information and query expression similarity,and a new method for clustering sparse data.

Search Result Diversification Based on Query Facets

In search engines, different users may search for different information by issuing the same query. To satisfy more users with limited search results, search result diversification re-ranks the results to cover as many user intents as possible. Most existing intent-aware diversification algorithms recognize user intents as subtopics, each of which is usually a word, a phrase, or a piece of description. In this paper, we leverage query facets to understand user intents in diversification, where each facet contains a group of words or phrases that explain an underlying intent of a query. We generate subtopics based on query facets and propose faceted diversification approaches. Experimental results on the public TREC 2009 dataset show that our faceted approaches outperform state-of-the-art diversification models.

Query Intent Disambiguation of Keyword-Based Semantic Entity Search in Dataspaces

Keyword query has attracted much research attention due to its simplicity and wide applications. The inherent ambiguity of keyword query is prone to unsatisfied query results. Moreover some existing techniques on Web query, keyword query in relational databases and XML databases cannot be completely applied to keyword query in dataspaces. So we propose KeymanticES, a novel keyword-based semantic entity search mechanism in dataspaces which combines both keyword query and semantic query features. And we focus on query intent disambiguation problem and propose a novel three-step approach to resolve it. Extensive experimental results show the effectiveness and correctness of our proposed approach.