@InProceedings{nikolentzos-EtAl:2017:EMNLP2017,
  author    = {Nikolentzos, Giannis  and  Meladianos, Polykarpos  and  Rousseau, Francois  and  Stavrakas, Yannis  and  Vazirgiannis, Michalis},
  title     = {Shortest-Path Graph Kernels for Document Similarity},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1890--1900},
  abstract  = {In this paper, we present a novel document similarity measure based on the
	definition of a graph kernel between pairs of documents. The proposed measure
	takes into account both the terms contained in the documents and the
	relationships between them. By representing each document as a graph-of-words,
	we are able to model these relationships and then determine how similar two
	documents are by using a modified shortest-path graph kernel. We evaluate our
	approach on two tasks and compare it against several baseline approaches using
	various performance metrics such as DET curves and macro-average F1-score.
	Experimental results on a range of datasets showed that our proposed approach
	outperforms traditional techniques and is capable of measuring more accurately
	the similarity between two documents.},
  url       = {https://www.aclweb.org/anthology/D17-1202}
}