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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Extracting Fine-Grained Entities Based on Coordinate Graph
verfasst von : Qing Yang, Peng Jiang, Chunxia Zhang, Zhendong Niu
Erschienen in: Natural Language Processing and Information Systems
Verlag: Springer Berlin Heidelberg
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Most previous entity extraction studies focus on a small set of coarse-grained classes, such as person etc. However, the distribution of entities within query logs of search engine indicates that users are more interested in a wider range of fine-grained entities, such as GRAMMY winner and Ivy League member etc. In this paper, we present a semi-supervised method to extract fine-grained entities from an open-domain corpus. We build a graph based on entities in
coordinate list
s, which are html nodes with the same tag path of the DOM trees. Then class labels are propagated over the graph from known entities to unknowns. Experiments on a large corpus from ClueWeb09a dataset show that our proposed approach achieves the promising results.