Random-walk models of term semantics: an application to opinion-related properties

It has recently been proposed that term senses can be automatically ranked by how strongly they possess a given opinion-related property, by applying PageRank, the well known random-walk algorithm lying at the basis of the Google search engine, to a graph in which nodes are represented by WordNet synsets and links are represented by the binary relation ``the gloss of synset s_i contains a term belonging to synset s_k}''. In other words, these properties are seen as ``flowing'' through this graph, from the definiendum} (i.e., the synset being defined) to the definiens (i.e., a synset which occurs in the gloss of the definiendum), with PageRank controlling the logic of this flow. In this paper we contend that two other random-walk algorithms may be equally adequate to this task, and provide an intuitive justification to support this claim. The first is a random-walk algorithm different from PageRank which we apply to the ``inverse'' graph, i.e., with properties flowing from the definiens to the definiendum. The second algorithm is a bidirectional random-walk algorithm, which assumes that properties may flow from the definiens to the definiendum and viceversa. We report results which significantly improve on the ones obtained by simple PageRank.