Paper: Evaluating Text Segmentation using Boundary Edit Distance

ACL ID P13-1167
Title Evaluating Text Segmentation using Boundary Edit Distance
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

This work proposes a new segmentation evaluation metric, named boundary simi- larity (B), an inter-coder agreement coef- ficient adaptation, and a confusion-matrix for segmentation that are all based upon an adaptation of the boundary edit distance in Fournier and Inkpen (2012). Existing seg- mentation metrics such as Pk, WindowD- iff, and Segmentation Similarity (S) are all able to award partial credit for near misses between boundaries, but are biased towards segmentations containing few or tightly clustered boundaries. Despite S?s improvements, its normalization also pro- duces cosmetically high values that over- estimate agreement & performance, lead- ing this work to propose a solution.