Timeline Generation Based on a Two-Stage Event-Time Anchoring Model
説明
Timeline construction task has become popular as a way of multi-document summarization. Dealing with such a problem, it is essential to anchor each event to an appropriate time expression in a document. In this paper, we present a supervised machine learning model, two-stage event-time anchoring model. In the first stage, our system estimates event-time relations using local features. In the second stage, the system re-estimates them using the result of first stage and global features. Our experimental results show that the proposed method surpasses the state-of-the-art system by 3.5 F-score points in the TimeLine shared task of SemEval 2015.