TextGraphs-6: Graph-based Methods for Natural Language Processing
From the website:
TextGraphs is at its SIXTH edition! This shows that two seemingly distinct disciplines, graph theoretic models and computational linguistics, are in fact intimately connected, with a large variety of Natural Language Processing (NLP) applications adopting efficient and elegant solutions from graph-theoretical framework. The TextGraphs workshop series addresses a broad spectrum of research areas and brings together specialists working on graph-based models and algorithms for NLP and computational linguistics, as well as on the theoretical foundations of related graph-based methods. This workshop series 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.
Special Theme: “Graphs in Structured Input/Output Learning”
Recent work in machine learning has provided interesting approaches to globally represent and process structures, e.g.:
- graphical models, which encode observations, labels and their dependencies as nodes and edges of graphs
- kernel-based machines which can encode graphs with structural kernels in the learning; algorithms
- SVM-struct and other max margin methods and the structured perceptron that allow for outputting entire structures like for example graphs
Important dates:
April 1, 2011 Submission deadline
April 25th, 2011 Notification of acceptance
May 6th, 2011 Camera-ready copies due
June 23th, 2011 Textgraphs workshop at ACL-HLT 2011
As if Neo4J and Gremlin weren’t enough of an incentive to be interested in graph approaches. đ