12th Workshop on Graph-Based Natural Language Processing

Workshop at NAACL-HLT 2018

New Orleans, Louisiana

June 6, 2018


The workshops in the TextGraphs series have published and promoted the synergy between the field of Graph Theory and Natural Language Processing. Besides traditional NLP applications like word sense disambiguation and semantic role labeling, and information extraction graph-based solutions nowadays also target new web-scale applications like information propagation in social networks, rumor proliferation, e-reputation, language dynamics learning, and future events prediction, to name a few.

The twelfth edition of the TextGraphs workshop aims to extend the focus on (1) issues and solutions for large-scale graphs, such as those derived for web-scale knowledge acquisition or social networks and (2) graph-based and graph-supported machine learning and deep learning methods. We encourage the description of novel NLP problems or applications that have emerged in recent years, which can be addressed with existing and new graph-based methods. Furthermore, we also encourage research on applications of graph-based methods in the area of Semantic Web in order to link them to related NLP problems and applications. .


TextGraphs-12 invites submissions on (but not limited to) the following topics:

  • Graph-based and graph-supported machine learning and deep learning methods
      • Graph embeddings
      • Graph-based and graph-supported deep learning (e.g., graph-based recurrent and recursive networks)
      • Probabilistic graphical models and structure learning methods
      • Graph-based methods for reasoning and interpreting deep neural networks
      • Exploration of capabilities and limitations of graph-based methods being applied to neural networks,
      • Investigation of aspects of neural networks that are (not) susceptible to graph-based analysis
  • 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 relation identification,
      • Encoding semantic distances in graphs,
      • Graph-based techniques for text summarization, simplification, and paraphrasing
      • Graph-based techniques for document navigation and visualization,
      • Reranking with graphs,
      • Applications of label propagation algorithms, 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
      • Graph kernels
  • Graph-based methods for applications on social networks
      • Rumor proliferation
      • E-reputation
      • Multiple identity detection
      • Language dynamics studies
      • Surveillance systems
  • Graph-based methods for NLP and Semantic Web
      • Representation learning methods for knowledge graphs (i.e., knowledge graph embedding)
      • Using graphs-based methods to populate ontologies using textual data
      • Inducing knowledge of ontologies into NLP applications using graphs
      • Merging ontologies with graph-based methods using NLP techniques