Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Semantic Neighborhoods as Hypergraphs

Chris Quirk and Pallavi Choudhury


Ambiguity preserving representations such as lattices are very useful in a number of NLP tasks, including paraphrase generation, paraphrase recognition, and machine translation evaluation. Lattices compactly represent lexical variation, but word order variation leads to a combinatorial explosion of states. We advocate hypergraphs as compact representations for sets of utterances describing the same event or object. We present a method to construct hypergraphs from sets of utterances, and evaluate this method on a simple recognition task. Given a set of utterances that describe a single object or event, we construct such a hypergraph, and demonstrate that it can recognize novel descriptions of the same event with high accuracy.


Publication typeInproceedings
Published inACL
> Publications > Semantic Neighborhoods as Hypergraphs