Extracting spatial information about events from text

Abstract

Automatic extraction of spatial information about events from text plays an important role not only in the semantic interpretation of events but also in many location-based applications such as infectious disease surveillance and natural disaster monitoring. However, the fundamental limitation of previous work is the limited scope of extraction that only targets at information that is explicitly stated through predicate-argument structures. This leads to missing a lot of implicit information inferable from context in a document, which amounts to nearly 40% of the entire location information. To overcome this limitation, we present in this dissertation an approach to recognizing the document-level relationship between events and their locations, aiming specifically at identifying an expression in text that best indicates where a given event occurs. We present a two-step approach to this problem: First, we design an annotation framework to construct a corpus annotated with the associations between event mentions and location expressions in news articles. Based on the corpus annotation and analysis, we hypothesize that coherent narratives such as news articles usually mention a series of events that occur together in a similar location. Second, we present an inference system that recognizes the associations from a given document based on this hypothesis. The system employs a multi-pass architecture where locally captured, more precise information is propagated to neighboring events through particular context. We exploit distributional similarities as key contextual information in this architecture to assess how similar two events are. The results of experiments on the annotated corpus demonstrate that the multi-pass architecture with distributional similarities is reasonably capable of capturing the document-level associations between events and locations, especially when compared with several baseline systems. The results also show that considering multiple types of event components together in modeling event similarities leads to better understanding of spatial relatedness of two events than just a single type of component. Our system achieves good performance for this challenging task, which is around F1-scores of 0.50 across different settings, considering that general state-of-the-art systems for extracting spatiotemporal relations and document-level event relations show a similar level of performance. We believe that the proposed corpus and system have a good potential not only to benefit many downstream NLP tasks that involve a spatial analysis of events, but also to improve the quality of location-based applications that exploit textual documents.