A methodology for the automatic construction of a hypertext for information retrieval
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
MindNet: acquiring and structuring semantic information from text
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Structural properties of lexical systems: monolingual and multilingual perspectives
MLRI '06 Proceedings of the Workshop on Multilingual Language Resources and Interoperability
Graph based semi-supervised approach for information extraction
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
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IBM Journal of Research and Development
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MuLLinG is a model for knowledge extraction (especially lexical extraction from corpora), based on multilevel graphs. Its aim is to allow large-scale data acquisition, by making it easy to realize automatically, and simple to configure by linguists with limited knowledge in computer programming. In MuLLinG, each new level represents the information in a different manner (more and more abstract). We also introduce several associated operators, written to be as generic as possible. They are independent of what nodes and edges represent, and of the task to achieve. Consequently, they allow the description of a complex extraction process as a succession of simple graph manipulations. Finally, we present an experiment of collocation extraction using MuLLinG model.