Entity Linking on Microblogs with Spatial and Temporal Signals

Yuan Fang, Ming-Wei Chang

Abstract


Microblogs present an excellent opportunity for monitoring and analyzing world happenings. Given that words are often ambiguous, entity linking becomes a crucial step towards understanding microblogs. In this paper, we re-examine the problem of entity linking on microblogs. We first observe that spatiotemporal (i.e., spatial and temporal) signals play a key role, but they are not utilized in existing approaches. Thus, we propose a novel entity linking framework that incorporates spatiotemporal signals through a weakly supervised process. Using entity annotations (Can be downloaded at http://research.microsoft.com/en-us/downloads/84ac9d88-c353-4059-97a4-87d129db0464/ ) on real-world data, our experiments show that the spatiotemporal model improves F1 by more than 10 points over existing systems. Finally, we present a qualitative study to visualize the effectiveness of our approach. 


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