[Elsnet-list] CfP - JNLE Special Issue on Graphs for NLP

Konstantinova, Natalia N.Konstantinova at wlv.ac.uk
Fri Feb 28 13:01:02 CET 2014

Journal of Natural Language Engineering Special Issue on Graphs for NLP

Call for Papers

The graph structure naturally models connections. In natural language processing, connections are ubiquitous, on anything between small and web-scale. We find them between words as grammatical, collocation or semantic relations; between clauses, sentences or larger text fragments as discourse, entailment, similarity or other relations; between complete texts or web pages; between entities in a social network; between concepts in ontologies or other knowledge repositories. Beyond the more often encountered regular graphs, hypergraphs have also appeared in our field, useful for capturing relations between more than two units.

Graphs have been rigorously studied, both mathematically and computationally. Such a well developed base could be, indeed has been, very useful for the field of Natural Language Processing: existing proofs and solutions could solve the problems that we manage to map into the graph framework. The relation need not be one way: the graph form of an NLP problem may lead to interesting computational research, maybe particularly when dealing with very large scale structures.

It is not surprising then that graphs occur often, and successfully, in NLP work. A concentrated evidence of this fact is the ongoing TextGraphs workshops. With the 9th edition taking place this year collocated with EMNLP 2014, this series of workshops has exposed and encouraged the synergy between the fields of Graph Theory and Natural Language Processing. The work presented shows a nice progression from small graphs that provided efficient and elegant solutions for NLP applications that focused on single documents for POS tagging, word sense disambiguation, or semantic role labeling, to increasingly larger structures for ontology learning through open IE, analysis of information propagation in social networks, to name but a few.

We think the time has arrived to summarize the most successful graph-based approaches to a variety of Natural Language Processing problems, both small and large scale, and everything in between.

Topics of Interest

To reflect the many applications of graph structure analysis in NLP, we propose the following topics, which cover the entire range from small sentence-level graphs to very large  web-scale graphs:

- 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.В

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

Guest Editors

Zornitsa Kozareva, Yahoo! Inc, USA
Vivi Nastaste, FBK, Italy
Rada Mihalcea, University of Michigan, USA

Important Dates

First call for papers: February 20, 2014
Second call for papers: April 1, 2014
Submissions: June 1, 2014
Initial decisions: August 15, 2014
Submission of revised versions: October 15, 2014
Final decisions: December 15, 2015
Submission of camera-ready versions: January 30, 2015



Scanned by iCritical.

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