TextGraphs -- the 9th edition

For the past 8 years, the series of TextGraphs workshops have exposed and encouraged the synergy between the field of Graph Theory (NLP) and Natural Language Processing (NLP). The mix between the two started small, with graph theoretical framework providing efficient and elegant solutions for NLP applications that focused on single documents for part-of-speech tagging, word sense disambiguation and semantic role labelling, got progressively larger with ontology learning and information extraction from large text collections, and have reached web scale through the new fields of research that focus on information propagation in social networks, rumor proliferation, e-reputation, multiple entity detection, language dynamics learning and future events prediction to name but a few.

The 9th edition of the TextGraphs workshop would be a new step in the series, focused on issues and solutions for large-scale graphs, such as those derived for web-scale knowledge acquisition or social networks. We aim to encourage the description of novel NLP problems or applications that have emerged in recent years which can be addressed with graph-based solutions, as well as novel graph-based methods that can be applied to known NLP tasks. Bringing together researchers interested in Graph Theory applied to Natural Language Processing will provide an environment for further integration of graph-based solutions into NLP tasks, and a deeper understanding of new theories of graph-based algorithms is likely to help create new approaches and widen the usage of graphs for NLP applications.

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Workshop Topics

TextGraphs-9 invites submissions on (but not limited to) the following topics:

• Graph-based methods for Information Retrieval, Information Extraction and Text Mining

  • Graph-based methods for word sense disambiguation,
  • Graph-based representations for ontology learning,
  • Graph-based strategies for semantic relations identification,
  • Encoding semantic distances in graphs,
  • Graph-based techniques for document navigation and visualization,
  • Reranking with graphs,
  • Label Propagation, etc.
  
  

• New graph-based methods for NLP applications

  • Random walk methods in graphs,
  • Spectral graph clustering,
  • Semi-supervised graph-based methods,
  • Methods and analyses for statistical networks,
  • Small world graphs,
  • Dynamic graph representations,
  • Topological and pretopological analysis of graphs, etc;
  
  

• Graph-based methods for applications on social networks

  • Rumor proliferation,
  • E-reputation,
  • Multiple identity detection,
  • Language dynamics studies,
  • Surveillance systems, etc.