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Inferring lexical type labels for entity mentions in texts is an important asset for NLP tasks like semantic role labeling and named entity disambiguation (NED). Prior work has focused on flat and relatively small type systems where most entities belong to exactly one type. This paper addresses very fine-grained types organized in a hierarchical taxonomy,(More)
Recent research has shown progress in achieving high-quality, very fine-grained type classification in hierarchical tax-onomies. Within such a multi-level type hierarchy with several hundreds of types at different levels, many entities naturally belong to multiple types. In order to achieve high-precision in type classification, current approaches are(More)
The advent of online social media and the growing popularity of sensor-equipped mobile devices have created a vast landscape of location-aware applications and services. This goldmine of data, including temporal and spatial information of unprecedented granularity, can help researchers gain insights into the behavioural patterns of people at a global scale.(More)
This paper describes a method for learning which relations are highly associated with a given seed relation such as marriage or working for a company. Relation instances taken from a large knowledge base are used as seeds for obtaining candidate sentences expressing the associated relations. Relations of interest are identified by parsing the sentences and(More)
This paper addresses a central sub-task of timeline creation from historical Wikipedia articles: learning from text which of the person names in a textual article should appear in a timeline on the same topic. We first process hundreds of timelines written by human experts and related Wikipedia articles to construct a corpus that can be used to evaluate(More)