SemantiKLUE: Robust Semantic Similarity at Multiple Levels Using Maximum Weight Matching
Thomas Proisl, Stefan Evert, Paul Greiner, Besim Kabashi · 2014
Being able to quantify the semantic similarity between two texts is important for many practical applications.SemantiKLUE combines unsupervised and supervised techniques into a robust system for measuring semantic similarity.At the core of the system is a word-to-word alignment of two texts using a maximum weight matching algorithm.The system participated in three SemEval-2014 shared tasks and the competitive results are evidence for its usability in that broad field of application.