Combining Temporal Information and Topic Modeling for Cross-Document Event Ordering
This work addresses event ordering in news analysis, but it is incremental as it builds on existing methods with noted drawbacks.
The paper tackled the problem of building unified timelines from news articles by combining temporal information and topic modeling for cross-document event ordering, achieving the highest Micro-average F-score results in SemEval2015 Task 4 with improvements of up to 6% over other systems.
Building unified timelines from a collection of written news articles requires cross-document event coreference resolution and temporal relation extraction. In this paper we present an approach event coreference resolution according to: a) similar temporal information, and b) similar semantic arguments. Temporal information is detected using an automatic temporal information system (TIPSem), while semantic information is represented by means of LDA Topic Modeling. The evaluation of our approach shows that it obtains the highest Micro-average F-score results in the SemEval2015 Task 4: TimeLine: Cross-Document Event Ordering (25.36\% for TrackB, 23.15\% for SubtrackB), with an improvement of up to 6\% in comparison to the other systems. However, our experiment also showed some draw-backs in the Topic Modeling approach that degrades performance of the system.