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Esuli A., Sebastiani F. Random-walk models of term semantics: an application to opinion-related properties. In: 3rd Language & Technology Conference (Poznan, Polonia, 5-7 Ottobre 2007). Proceedings, pp. 221 - 225. Wydawnictwo Poznańskie Sp. z o. o, 2007.
 
 
Abstract
(English)
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.
Subject Random Walk Models
Term Semantics
Opinion Mining
I.2.7 Natural Language Processing. Language models
G.3 Probability and Statistics. Markov processes


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