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• W2912738952 abstract "The last few years have shown a steady increase in applying graph-theoretic models to computational linguistics. In many NLP applications, entities can be naturally represented as nodes in a graph and relations between them can be represented as edges. There have been extensive research showing that graph-based representations of linguistic units such 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 labeling, summarization, and sentiment analysis.More recently, complex network theory, a popular modeling paradigm in statistical mechanics and physics of complex systems, was proven to be a promising tool in understanding the structure and dynamics of languages. Complex network based models have been applied to areas as diverse as language evolution, acquisition, historical linguistics, mining and analyzing the social networks of blogs and emails, link analysis and information retrieval, information extraction, and representation of the mental lexicon. In order to make this field of research more visible, this time the workshop incorporated a special theme on Cognitive and Social Dynamics of Languages in the framework of Complex Networks. Cognitive dynamics of languages include topics focused primarily on language acquisition, which can be extended to language change (historical linguistics) and language evolution as well. Since the latter phenomena are also governed by social factors, we can further classify them under social dynamics of languages. In addition, social dynamics of languages also include topics such as mining the social networks of blogs and emails. A collection of articles pertaining to this special theme will be compiled in a special issue of the Computer Speech and Language journal.This volume contains papers accepted for presentation at the TextGraphs-4 2009 Workshop on Graph-Based Methods for Natural Language Processing. The event took place on August 7, 2009, in Suntec, Singapore, immediately following ACL/IJCNLP 2009, the Joint conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing. Being the fourth workshop on this topic, we were able to build on the success of the previous TextGraphs workshops, held as part of HLT-NAACL 2006, HLT-NAACL 2007 and Coling 2008. It aimed at bringing together researchers working on problems related to the use of graph-based algorithms for NLP and on pure graph-theoretic methods, as well as those applying complex networks for explaining language dynamics. Like last year, TextGraphs-4 has also been endorsed by SIGLEX.We issued calls for both regular and short papers. Nine regular and three short papers were accepted for presentation, based on the careful reviews of our program committee. Our sincere thanks to all the program committee members for their thoughtful, high quality and elaborate reviews, especially considering our extremely tight time frame for reviewing. The papers appearing in this volume have surely benefited from their expert feedback. This year's workshop attracted papers employing graphs in a wide range of settings and we are therefore proud to present a very diverse program. We received quite a few papers on discovering semantic similarity through random walks. Daniel Ramage et al. explore random walk based methods to discover semantic similarity in texts, while Eric Yeh et al. attempt to discover semantic relatedness through random walks on the Wikipedia. Amec Herdagdelen et al. describes a method for measuring semantic relatedness with vector space models and random walks." @default.
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• W2912738952 date "2009-08-07" @default.
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• W2912738952 title "Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing" @default.
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