Both vector space models and graph random walk models can be used to determine similarity between concepts. Noting that vectors can be regarded as local views of a graph, we directly compare vector space models and graph random walk models on standard tasks of predicting human similarity ratings, concept categorization, and semantic priming, varying the size of the dataset from which vector space and graph are extracted.
In REF ) vector space models were compared, by embedding them in graphs, and graph random walk models in order to determine similarity between concepts, and showed that some random walk models can achieve results as good as or even better than the vector models.
Measuring semantic relatedness with vector space models and random walks
Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4)
Computer Science Mathematics