Abstract
This paper describes an improved method for relation identification, which is the last step of unsupervised relation extraction. Similar entity pairs maybe grouped into the same cluster. It is also important to select a key word to describe the relation accurately. Therefore, an improved DF feature selection method is employed to rearrange low-frequency entity pairs’ features in order to get a feature set for each cluster. Then we used an improved Discriminative Category Matching (DCM) method to select typical and discriminative words for entity pairs’ relation. Our experimental results show that Improved DCM method is better than the original DCM method in relation identification.
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Sun, Y., Yang, J., Lin, X. (2013). An Improved Discriminative Category Matching in Relation Identification. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_39
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DOI: https://doi.org/10.1007/978-3-642-38824-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38823-1
Online ISBN: 978-3-642-38824-8
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