Deep News Event Ranker Based On User Relevant Query
文献类型：会议论文
| 作者 | 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)
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| 出版日期 | 2018-08 |
| 会议日期 | April 20-22, 2018 |
| 会议地点 | Chengdu, China |
| 关键词 | News Event Ranker User Relevant Query User Related Subjective Aspects Deep News Event Ranker |
| 页码 | 363-367 |
| 英文摘要 | 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 queryndocument 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. |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21031]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 通讯作者 | Kong QC(孔庆超) |
| 作者单位 | 1.中国科学院大学 2.中科院自动化所 3.北京大学 |
| 推荐引用方式 GB/T 7714 | Kong XF,Kong QC,Mao WJ,et al. Deep News Event Ranker Based On User Relevant Query[C]. 见:. Chengdu, China. April 20-22, 2018. |
入库方式： OAI收割
来源：自动化研究所
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