NAACL/HLT 2007 Workshop
Graph-based Methods for Natural Language Processing
http://www.textgraphs.org/ws07
Rochester, NY, April 26, 2007
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Recent years have shown an increased interest in bringing the field of
graph theory into Natural Language Processing. In many NLP
applications entities can be naturally represented as nodes in a graph
and relations between them can be represented as edges. Recent
research has shown that graph-based representations of linguistic
units as diverse as words, sentences and documents give rise to novel
and efficient solutions in a variety of NLP tasks, ranging from part
of speech tagging, word sense disambiguation and parsing to
information extraction, semantic role assignment, summarization and
sentiment analysis.
This workshop builds on the success of the first TextGraphs workshop at
Graph-based Methods for Natural Language Processing
http://www.textgraphs.org/ws07
Rochester, NY, April 26, 2007
.................................................
Recent years have shown an increased interest in bringing the field of
graph theory into Natural Language Processing. In many NLP
applications entities can be naturally represented as nodes in a graph
and relations between them can be represented as edges. Recent
research has shown that graph-based representations of linguistic
units as diverse as words, sentences and documents give rise to novel
and efficient solutions in a variety of NLP tasks, ranging from part
of speech tagging, word sense disambiguation and parsing to
information extraction, semantic role assignment, summarization and
sentiment analysis.
This workshop builds on the success of the first TextGraphs workshop at
HLT-NAACL 2006. The aim of this workshop is to bring together researchers
working on problems related to the use of graph-based algorithms for natural
language processing and on the theory of graph-based methods.
It will address a broader spectrum of research areas to foster
exchange of ideas and help to identify principles of using the graph
notions that go beyond an ad-hoc usage.
Unveiling these principles will give rise to applying generic graph
methods to many new problems that can be encoded in this framework.
We invite submissions of papers on graph-based methods applied to
NLP-related problems. Topics include, but are not limited to:
- Graph representations for ontology learning and word sense disambiguation
- Graph algorithms for Information Retrieval, text mining and understanding
- Graph matching for Information Extraction
- Random walk graph methods and Spectral graph clustering
- Graph labeling and edge labeling for semantic representations
- Encoding semantic distances in graphs
- Ranking algorithms based on graphs
- Small world graphs in natural language
- Semi-supervised graph-based methods
- Statistical network analysis and methods for NLP
Submission format:
Submissions will consist of regular full papers of max. 8 pages and
short papers of max. 4 pages, formatted following the NAACL 2007
guidelines. Papers should be submitted using the online submission
form: http://www.cs.rochester.edu/meetings/hlt-naacl07/workshops.shtml
Important dates:
Regular paper submission January 29
Short paper submissions February 4
Notification of acceptance February 22
Camera-ready papers March 1
Workshop April 26
language processing and on the theory of graph-based methods.
It will address a broader spectrum of research areas to foster
exchange of ideas and help to identify principles of using the graph
notions that go beyond an ad-hoc usage.
Unveiling these principles will give rise to applying generic graph
methods to many new problems that can be encoded in this framework.
We invite submissions of papers on graph-based methods applied to
NLP-related problems. Topics include, but are not limited to:
- Graph representations for ontology learning and word sense disambiguation
- Graph algorithms for Information Retrieval, text mining and understanding
- Graph matching for Information Extraction
- Random walk graph methods and Spectral graph clustering
- Graph labeling and edge labeling for semantic representations
- Encoding semantic distances in graphs
- Ranking algorithms based on graphs
- Small world graphs in natural language
- Semi-supervised graph-based methods
- Statistical network analysis and methods for NLP
Submission format:
Submissions will consist of regular full papers of max. 8 pages and
short papers of max. 4 pages, formatted following the NAACL 2007
guidelines. Papers should be submitted using the online submission
form: http://www.cs.rochester.edu/meetings/hlt-naacl07/workshops.shtml
Important dates:
Regular paper submission January 29
Short paper submissions February 4
Notification of acceptance February 22
Camera-ready papers March 1
Workshop April 26
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