Abstract
Event information retrieval (EIR) is the task of retrieving news articles in response to the event-oriented query, which tailors for dealing with a large scale of news articles on breaking news (e.g., natural disaster). However, the challenges caused by the dynamic and complex natures of the event make EIR less studied than traditional information retrieval (IR). The existing studies usually employed either traditional IR models or machine learning approaches over hand-crafted features to retrieve events, which fails to capture more-complex structures of events with event evolution well. To address this issue, we exploit neural models incorporating the characteristics of events to EIR task. Experimental results demonstrate the effectiveness of our method over two datasets, which can regard as the simple but strong baseline for further research in EIR.
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