| ISBN: 978-1-59593-828-2
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| description |
In this paper, we present a new approach for recognition and
grounding of geographic proper names for German. Named Entity
Recognition (NER) in German is more difficult than in English
because not only proper names, but all nouns start with capital
letters, which results in a large pool of potential ambiguous
entities. Our approach makes critical use of a geographic knowledge
base that is more detailed (down to the level of streets) and more
structured than most knowledge bases used before. We have designed a
three-step model (spotting, typing, referencing) that specifies the
sources of information that are necessary for geo-tagging and their
dependency relationships. Basic aspects of the model were
implemented and evaluated in a proof of concept. The model can be
applied to other NER tasks by simply substituting the appropriate
knowledge base for the one used here and retraining the model.
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| publisher |
University of Stuttgart : Collaborative Research Center SFB 627
(Nexus: World Models for Mobile Context-Based
Systems)
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| New York: ACM
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| type |
Text
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| Article in Proceedings
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| source |
In: Proceedings of the 4th ACM Workshop On Geographic Information
Retrieval, pp. 25-30
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| contributor |
Institut für Maschinelle Sprachverarbeitung
(IMS)
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| subject |
Information Search and Retrieval (CR H.3.3)
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