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.
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Tsoi, A.C., Hagenbuchner, M., Chau, R., Lee, V. (2009). Unsupervised and Supervised Learning of Graph Domains. In: Bianchini, M., Maggini, M., Scarselli, F., Jain, L.C. (eds) Innovations in Neural Information Paradigms and Applications. Studies in Computational Intelligence, vol 247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04003-0_3
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DOI: https://doi.org/10.1007/978-3-642-04003-0_3
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