acl acl2013 acl2013-140 acl2013-140-reference knowledge-graph by maker-knowledge-mining

140 acl-2013-Evaluating Text Segmentation using Boundary Edit Distance


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Author: Chris Fournier

Abstract: This work proposes a new segmentation evaluation metric, named boundary similarity (B), an inter-coder agreement coefficient 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 segmentation metrics such as Pk, WindowDiff, 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 produces cosmetically high values that overestimate agreement & performance, leading this work to propose a solution.


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