Paper: Segmentation Similarity and Agreement

ACL ID N12-1016
Title Segmentation Similarity and Agreement
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012
Authors

We propose a new segmentation evaluation metric, called segmentation similarity (S), that quantifies the similarity between two segmen- tations as the proportion of boundaries that are not transformed when comparing them us- ing edit distance, essentially using edit dis- tance as a penalty function and scaling penal- ties by segmentation size. We propose several adapted inter-annotator agreement coefficients which use S that are suitable for segmenta- tion. We show that S is configurable enough to suit a wide variety of segmentation evalua- tions, and is an improvement upon the state of the art. We also propose using inter-annotator agreement coefficients to evaluate automatic segmenters in terms of human performance.