Attention Values of Events
Top 5 and bottom 5 sentences ranked by attentionvalues from the event embedding learning module. We find some events are more/less relevant to celebrities's trip.
Abstract
Celebrities’ whereabouts are of pervasive importance. For instance, where politicians go, how often they visit, and who they meet, come with profound geopolitical and economic implications.
Although news articles contain travel information of celebrities, it is not possible to perform large-scale and network-wise analysis due to the lack of automatic itinerary detection tools. To design such tools, we have to overcome difficulties from the heterogeneity among news articles.
We model text content across articles related to each candidate location as a graph to better associate essential information and cancel out the noises. The proposed CeleTrip jointly trains these modules, which outperforms all baseline models and achieves 82.53% in the F1 metric.
By open-sourcing the first tool and a carefully curated dataset for such a task, we hope to facilitate relevant research in celebrity itinerary mining as well as the social and political analysis built upon the extracted trips.
Architecture of CeleTrip
CeleTrip learns the overall representations of candidate locations and classifies them in Trip Graph, where the textual description of each candidate location is incorporated by the Location Embedding Learning, the knowledge of related entities is supplied by Entity Embedding Learning, and the information of related events is obtained by Event Embedding Learning.
