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| Content Provider | ACM Digital Library |
|---|---|
| Author | Spitz, Andreas Gertz, Michael |
| Abstract | Real world events, such as historic incidents, typically contain both spatial and temporal aspects and involve a specific group of persons. This is reflected in the descriptions of events in textual sources, which contain mentions of named entities and dates. Given a large collection of documents, however, such descriptions may be incomplete in a single document, or spread across multiple documents. In these cases, it is beneficial to leverage partial information about the entities that are involved in an event to extract missing information. In this paper, we introduce the LOAD model for cross-document event extraction in large-scale document collections. The graph-based model relies on co-occurrences of named entities belonging to the classes locations, organizations, actors, and dates and puts them in the context of surrounding terms. As such, the model allows for efficient queries and can be updated incrementally in negligible time to reflect changes to the underlying document collection. We discuss the versatility of this approach for event summarization, the completion of partial event information, and the extraction of descriptions for named entities and dates. We create and provide a LOAD graph for the documents in the English Wikipedia from named entities extracted by state-of-the-art NER tools. Based on an evaluation set of historic data that include summaries of diverse events, we evaluate the resulting graph. We find that the model not only allows for near real-time retrieval of information from the underlying document collection, but also provides a comprehensive framework for browsing and summarizing event data. |
| Starting Page | 503 |
| Ending Page | 512 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450340694 |
| DOI | 10.1145/2911451.2911529 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-07-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Document indexing Named entities Ranking Entity linking Event representation Summarization Event extraction |
| Content Type | Text |
| Resource Type | Article |
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