iKernels

Machine Learning and NLP group at Trento.

TextGraphs-5

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Recent years have shown an increased amount of interest in applying graph theoretic models to computational linguistics. Both graph theory and computational linguistics are well studied disciplines, which have traditionally been perceived as distinct, embracing different algorithms, different applications, and different potential end-users. However, as recent research work has shown, the two seemingly distinct disciplines are in fact intimately connected, with a large variety of Natural Language Processing (NLP) applications adopting effcient and elegant solutions from graph-theoretical framework.

Traditional graph theory, which is studied as a sub-discipline of mathematics, as well as complex network theory, a popular modeling paradigm in statistical mechanics and physics of complex systems, have been successfully applied in modeling and solving several applications in NLP. These disciplines are proven to be a promising tool in understanding the structure and dynamics of languages. Graphs are natural ways to encode information for NLP. Entities can be naturally represented as nodes 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 tasks, ranging from part-of-speech tagging, word sense disambiguation and parsing to information extraction, semantic role labeling, summarization, and sentiment analysis. Complex network-based models have been applied to areas as diverse as language evolution, acquisition, historical linguistics, mining and analyzing the social networks of blogs and emails, link analysis and information retrieval, information extraction, and representation of the mental lexicon.

The TextGraphs workshop series addresses a broad spectrum of research areas and brings together specialists working on graph-based models and algorithms for natural language processing and computational linguistics, as well as on the theoretical foundations of related graph-based methods. This workshop is aimed at fostering an exchange of ideas by facilitating a discussion about both the techniques and the theoretical justification of the empirical results among the NLP community members. Spawning a deeper understanding of the basic theoretical principles involved, such interaction is vital to the further progress of graph-based NLP applications.