Since the early ages of artificial intelligence, associative 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 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 talk, I will present a new framework for the application of graph-based ranking algorithms to structures derived from text, and show how the synergy between graph-theoretical algorithms and graph-based text representations can result in efficient unsupervised methods for several natural language processing tasks. I will illustrate this framework with several text processing applications, including extractive summarization, word sense disambiguation, and keyphrase extraction. I will also outline a number of other applications that can find successful solutions within this framework, and conclude with a discussion of opportunities and challenges for future research.