MuLLinG: MultiLevel Linguistic Graphs for Knowledge Extraction
Vincent Archer · 2010
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. 1