Acceso usuarios
Rogelio Nazar, Jorge Vivaldi Palatresi 
, Leo Wanner
Los grafos de coocurrencia lexica han sido utilizados en lingustica computacional en experimentos de desambiguacion de sentidos pero hasta ahora no para la extraccion de relaciones de hiperonimia, donde la metodologa mas usual ha sido la aplicacion de patrones lexico-sintacticos. En este artculo mostramos que es posible extraer relaciones de hiperonimia entre terminos utilizando estadsticas de coocurrencia. La clave del metodo reside en que las relaciones de coocurrencia no suelen ser simetricas en el caso de las relaciones de hiperonimia y, en consecuencia, es posible generar grafos dirigidos de coocurrencia que guardan una apariencia similar a la de una taxonoma. En el presente artculo presentamos experimentos con textos de la Wikipedia en castellano ordenados aleatoriamente, pero los resultados sugieren que la coocurrencia asimetrica entre terminos es una propiedad intrnseca y macroscopica del discurso argumentativo en general.
Word co-occurrence graphs have been used in computational linguistics mainly for word sense disambiguation and induction, but until very recently, not for the extraction of hypernymy relations, where the methodology most often applied is the use of lexico-syntactic patterns. In this paper, we show that it is possible to use word co-occurrence statistics to extract IS-A relations between entities in scienti c and technical corpora. We exploit the fact that word co-occurrence often has a direction, that is, a term might co-occur with another, but this is very often not true the other way round. This means that one can represent co-occurrence as a directed graph and this graph resembles a taxonomy. In this paper we present an experiment with texts randomly extracted from the Spanish Wikipedia, but our ndings suggest that this co-occurrence behavior is a macroscopic and intrinsic property of argumentative discourse in general.
