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Unsupervised and supervised learning of graph domains

Chapter


Abstract


  • In this chapter, we will describe a method for extracting an underlying graph structure from an unstructured text document. The resulting graph structure is a symmetrical un-directed graph. An unsupervised learning approach is applied to cluster a given text corpus into groups of similar structured graphs. Moreover, if labels are given to some of the documents in the text corpus, a supervised learning approach can be applied to learn the underlying input-output mapping between the symmetrical un-directed graph structures and a real-valued vector. The approach will be illustrated using a standard benchmark problem in text processing, viz., a subset of the Reuters text corpus. Some observations and further research directions are given. © 2009 Springer-Verlag Berlin Heidelberg.

Publication Date


  • 2009

Citation


  • Tsoi, A. C., Hagenbuchner, M., Chau, R., & Lee, V. (2009). Unsupervised and supervised learning of graph domains. In Unknown Book (Vol. 247, pp. 43-65). doi:10.1007/978-3-642-04003-0_3

International Standard Book Number (isbn) 13


  • 9783642040023

Scopus Eid


  • 2-s2.0-70350225331

Book Title


  • Studies in Computational Intelligence

Start Page


  • 43

End Page


  • 65

Abstract


  • In this chapter, we will describe a method for extracting an underlying graph structure from an unstructured text document. The resulting graph structure is a symmetrical un-directed graph. An unsupervised learning approach is applied to cluster a given text corpus into groups of similar structured graphs. Moreover, if labels are given to some of the documents in the text corpus, a supervised learning approach can be applied to learn the underlying input-output mapping between the symmetrical un-directed graph structures and a real-valued vector. The approach will be illustrated using a standard benchmark problem in text processing, viz., a subset of the Reuters text corpus. Some observations and further research directions are given. © 2009 Springer-Verlag Berlin Heidelberg.

Publication Date


  • 2009

Citation


  • Tsoi, A. C., Hagenbuchner, M., Chau, R., & Lee, V. (2009). Unsupervised and supervised learning of graph domains. In Unknown Book (Vol. 247, pp. 43-65). doi:10.1007/978-3-642-04003-0_3

International Standard Book Number (isbn) 13


  • 9783642040023

Scopus Eid


  • 2-s2.0-70350225331

Book Title


  • Studies in Computational Intelligence

Start Page


  • 43

End Page


  • 65