Revisiting Lexicon Evaluation in Unsupervised Word Discovery

The paper critiques the normalized edit distance metric used for evaluating lexicons derived from unsupervised word discovery, noting its bias toward large clusters and its failure to account for the distribution of true classes across clusters. It proposes two new metrics—one that weights cluster size when measuring within‑cluster consistency and another that evaluates how true words are spread across clusters—drawing on clustering theory. Experiments on synthetic and real‑world lexicons show that these combined metrics better correlate with ground‑truth distributions and are more robust to evaluation biases.