Abstract
In this paper, we study the problem of unsupervised relation extraction based on model order identification and discriminative feature analysis. The model order identification is achieved by stability-based clustering and used to infer the number of the relation types between entity pairs automatically. The discriminative feature analysis is used to find discriminative feature words to name the relation types. Experiments on ACE corpus show that the method is promising.
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Jinxiu, C., Donghong, J., Lim, T.C., Zhengyu, N. (2005). Automatic Relation Extraction with Model Order Selection and Discriminative Label Identification. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_35
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DOI: https://doi.org/10.1007/11562214_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29172-5
Online ISBN: 978-3-540-31724-1
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