Graph-based Methods for Natural Language Processing(TextGraphs 2017)

August 3-4, 2017, Vancouver, Canada

Call for Papers :

The eleventh edition of the TextGraphs workshop aims to extend the focus on issues and solutions for large-scale graphs, such as those derived for web-scale knowledge acquisition or social networks. We encourage the description of novel NLP problems or applications that have emerged in recent years, which can be addressed with 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.

Topic of Interest :

• Graph-based methods for providing reasoning and interpretation of deep learning methods


• Graph-based methods for reasoning and interpreting deep processing by neural networks,
• Explorations of the capabilities and limits of graph-based methods applied to neural networks in general
• Investigation of which aspects of neural networks are not susceptible to graph-based methods.


• 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 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, etc.


• Graph-based methods for applications on social networks
• Rumor proliferation
• E-reputation
• Multiple identity detection
• Language dynamics studies
• Surveillance systems, etc.


• 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.

Important Dates :

• Paper submission: April 21, 2017
• Notification of acceptance: May 19, 2017
• Camera-ready submission: May 26, 2017
• Workshop date: August 3 or 4, 2017

User Name : jerish
Posted 19-01-2017 on 10:17:10 AEDT

Related CFPs

IJCACS   International Journal of Control, Automation, Communication and Systems
IJWesT   International journal of Web & Semantic Technology