Shortest-Path Graph Kernels for Document Similarity
Giannis Nikolentzos, Polykarpos Meladianos, Francois Waldeck Rousseau, Yannis Stavrakas, Michalis Vazirgiannis · 2017
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.