  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: 1)One single article can be noisy, with irrelevant people
and locations, especially when the articles are long. 2)Though it may be
helpful if we consider multiple articles together to determine a particular
trip, the key semantics are still scattered across different articles
intertwined with various noises, making it hard to aggregate them effectively.
3)Over 20% of the articles refer to the celebrities' trips indirectly, instead
of using the exact celebrity names or location names, leading to large portions
of trips escaping regular detecting algorithms. We model text content across
articles related to each candidate location as a graph to better associate
essential information and cancel out the noises. Besides, we design a special
pooling layer based on attention mechanism and node similarity, reducing
irrelevant information from longer articles. To make up the missing information
resulted from indirect mentions, we construct knowledge sub-graphs for named
entities (person, organization, facility, etc.). Specifically, we dynamically
update embeddings of event entities like the G7 summit from news descriptions
since the properties (date and location) of the event change each time, which
is not captured by the pre-trained event representations. The proposed CeleTrip
jointly trains these modules, which outperforms all baseline models and
achieves 82.53% in the F1 metric.
