Abstract
Named Entity Recognition became the basis of many Natural Language Processing applications. However, the existing coarse-grained named entity recognizers are insufficient for complex applications such as Question Answering, Internet Search engines or Ontology population. In this paper, we propose a domain distribution approach according to which names which occur in the same domains belong to the same fine-grained category. For our study, we generate a relevant domain resource by mapping and ranking the words from the WordNet glosses to their WordNetDomains. This approach allows us to capture the semantic information of the context around the named entity and thus to discover the corresponding fine-grained name category. The presented approach is evaluated with six different person names and it reaches 73% f-score. The obtained results are encouraging and perform significantly better than a majority baseline.
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Kozareva, Z., Vazquez, S., Montoyo, A. (2008). Domain Information for Fine-Grained Person Name Categorization. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2008. Lecture Notes in Computer Science, vol 4919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78135-6_26
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DOI: https://doi.org/10.1007/978-3-540-78135-6_26
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