NDLI: A novel clustering algorithm for Unsupervised Relation Extraction

Content Provider	IEEE Xplore Digital Library
Author	Jing Wang Yang Jing Yue Teng Qingling Li
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Technology, East China Normal University, Shanghai, China (Jing Wang; Yang Jing; Yue Teng; Qingling Li)
Abstract	Clustering of entity pairs is the core content of the unsupervised relation extraction method. However, most of the clustering algorithm in the previous unsupervised relation extraction does not take into account the influence of the duality between entity pairs and the relationship characteristics on clustering results. In order to overcome this defect, this paper proposed a novel clustering algorithm for unsupervised relation extraction. It introduces co-clustering theory on the basis of the k-means clustering, not only clustering the entity pairs but also clustering the relationship characteristics to make full use of the duality of the clustering dataset. The final experimental results demonstrate that our clustering algorithm get more higher accuracy rate than k-means clustering algorithm in unsupervised relation extraction.
Starting Page	16
Ending Page	21
File Size	383802
Page Count	6
File Format	PDF
ISBN	9781467324281
e-ISBN	9781467324304
DOI	10.1109/ICDIM.2012.6360156
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-22
Publisher Place	Macao
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis co-clustering theory Clustering methods Clustering algorithms Feature extraction unsupervised relation extraction Vectors Data mining Indexes duality
Content Type	Text
Resource Type	Article

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