Random walks on text structures

Abstract

Since the early ages of artificial intelligence, associative or semantic networks have been proposed as representations that enable the storage of language units and the relationships that interconnect them, allowing for a variety of inference and reasoning processes, and simulating some of the functionalities of the human mind. The symbolic structures that emerge from these representations correspond naturally to graphs - relational structures capable of encoding the meaning and structure of a cohesive text, following closely the associative or semantic memory representations. The activation or ranking of nodes in such graph structures mimics to some extent the functioning of human memory, and can be turned into a rich source of knowledge useful for several language processing applications. In this paper, we suggest a framework for the application of graph-based ranking algorithms to natural language processing, and illustrate the application of this framework to two traditionally difficult text processing tasks: word sense disambiguation and text summarization. © Springer-Verlag Berlin Heidelberg 2006.