Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification
Konstantinos Skianis, Fragkiskos D. Malliaros, Michalis Vazirgiannis · 2018
Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words (GoW) model in which each document is represented by a graph that encodes relationships between the different terms.Based on this formulation, the importance of a term is determined by weighting the corresponding node in the document, collection and label graphs, using node centrality criteria.We also introduce novel graph-based weighting schemes by enriching graphs with wordembedding similarities, in order to reward or penalize semantic relationships.Our methods produce more discriminative feature weights for text categorization, outperforming existing frequency-based criteria.Code and data are available online 1 .