Segmentation Similarity and Agreement

Details Segmentation Similarity and Agreement Segmentation Similarity and Agreement

2012-04-12

arxiv.org/abs/1204.2847v1

Proceedings of Human Language Technologies: The 2012 Annual Conference of the North American Chapter of the Association for Co

Computer Science

Computation and Language

10 pages, Proceedings of Human Language Technologies: The 2012 Annual Conference of the North American Chapter of the Associat

Scientific paper

We propose a new segmentation evaluation metric, called segmentation similarity (S), that quantifies the similarity between two segmentations as the proportion of boundaries that are not transformed when comparing them using edit distance, essentially using edit distance as a penalty function and scaling penalties by segmentation size. We propose several adapted inter-annotator agreement coefficients which use S that are suitable for segmentation. We show that S is configurable enough to suit a wide variety of segmentation evaluations, 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.

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