Networks and Natural Language Processing

Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work. Since the early ages of artificial intelligence, associative or semantic networks have been proposed as representations that enable the storage of such language units and the relations that interconnect them and that allow for a variety of inference and reasoning processes, simulating some of the functionalities of the human mind. The symbolic structures that emerge from these representations correspond naturally to graphs--where text constituents are represented as vertices and their interconnecting relations form the edges in the graph.