Recent years have shown an increased interest in bringing the field of graph theory into Natural Language Processing. In many NLP applications entities can be naturally represented as nodes in a graph and relations between them can be represented as edges. Recent research has shown that graph-based representations of linguistic units as diverse as words, sentences and documents give rise to novel and efficient solutions in a variety of NLP tasks, ranging from part of speech tagging, word sense disambiguation and parsing to information extraction, semantic role assignment, summarization and sentiment analysis.
The workshop aimed at bringing together researchers working on problems related to the use of graph-based algorithms for Natural Language Processing and on the theory of graph-based methods. It addressed a broad spectrum of research areas to foster exchange of ideas and help to identify principles of using the graph notions that go beyond an ad-hoc usage. Unveiling these principles will give rise to applying generic graph methods to many new problems that can be encoded in this framework.