Deep News Event Ranker Based On User Relevant Query
Kong XF(孔祥飞)1,2; Kong QC(孔庆超)1; Mao WJ(毛文吉)1,2; Tang SQ(唐少强)3; Kong QC(孔庆超)
Conference NameInternational Conference on Cloud Computing and Big Data Analysis
Conference DateApril 20-22, 2018
Conference PlaceChengdu, China
Abstract       News Event Ranking(NER), which takes eventrelated news documents as the ranking unit, has been addressed in many research work and implemented in securityoriented applications(e.g. public event monitoring, mining and retrieval). Previous work solely rank news event based on event relevant information, while user relevant information equally
important for characterizing news event is totally neglected. In this paper, we depict news event with extra user comments sentiment polarity information, and address news event ranking problem by incorporating user relevant information into the input query. Given an input query, which contains event related objective aspects(e.g. actors, locations, date) and user related subjective aspects(e.g. public attention and opinion polarity), we develop a Deep News Event Ranker model to integrate objective event information and subjective user information. Firstly, a semantic similarity interaction module transforms query keywords, news document and news comments to their semantic vector representation and calculates query
ndocument similarity and queryncomment similarity. Then a Feature Extraction Based On CNNs and LSTM module extract query term importance features, query term frequency features and BM25-like relevance features for ranking. Finally, a Feature Aggregation module merges the extracted features with some auxiliary relevance features and produces a global relevance score. Experiments on a large news dataset demonstrate the effectiveness of our proposed model compared to several baseline models.
KeywordNews Event Ranker User Relevant Query User Related Subjective Aspects Deep News Event Ranker
Indexed ByEI
Document Type会议论文
Corresponding AuthorKong QC(孔庆超)
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kong XF,Kong QC,Mao WJ,et al. Deep News Event Ranker Based On User Relevant Query[C],2018:363-367.
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