We present a method for constructing taxonomic trees (e.g., WordNet) using
pretrained language models. Our approach is composed of two modules, one that
predicts parenthood relations and another that reconciles those predictions
into trees. The parenthood prediction module produces likelihood scores for
each potential parent-child pair, creating a graph of parent-child relation
scores. The tree reconciliation module treats the task as a graph optimization
problem and outputs the maximum spanning tree of this graph. We train our model
on subtrees sampled from WordNet, and test on non-overlapping WordNet subtrees.
We show that incorporating web-retrieved glosses can further improve
performance. On the task of constructing subtrees of English WordNet, the model
achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best
published result on this task. In addition, we convert the original English
dataset into nine other languages using Open Multilingual WordNet and extend
our results across these languages.

本研究提出了一种使用预训练语言模型构建分类树（例如，WordNet）的方法，该方法包括预测父子关系和协调这些预测结果以形成树的两个模块。研究表明，该方法在构建英语 WordNet 子树的任务上取得了很好的成果，并在 Open Multilingual WordNet 上将结果扩展到其他九种语言。