基于上下文嵌入多分辨率网格的无监督单词多义性量化
Unsupervised Word Polysemy Quantification with Multiresolution Grids of Contextual Embeddings
AI总结：
本研究提出一种基于上下文嵌入空间几何特性的无监督单词多义性量化方法，在多个人工资源排名上验证了强相关性，可采样多义句且适用于任意语言。
摘要：
单词的义项数量即多义性是一个极具主观性的概念，在不同标注者和资源间差异很大。我们提出一种基于上下文嵌入空间简单几何特性的多义性估计新方法，该方法完全无监督且纯数据驱动。通过严格实验证明，我们的排名与来自WordNet、OntoNotes、牛津词典、维基百科等知名人工构建资源的6种不同排名，在6种标准指标下均具有强统计显著性的良好相关性。我们还对人工排名间的相关性进行了可视化与分析。该方法的一个有价值的副产品是无需额外成本即可采样包含给定单词不同义项的句子。最后，其完全无监督的特性使其可应用于任何语言。代码和数据公开于https://github.com/ksipos/polysemy-assessment，本文被EACL 2021接收为长论文。
英文摘要：
The number of senses of a given word, or polysemy, is a very subjective notion, which varies widely across annotators and resources. We propose a novel method to estimate polysemy, based on simple geometry in the contextual embedding space. Our approach is fully unsupervised and purely data-driven. We show through rigorous experiments that our rankings are well correlated (with strong statistical significance) with 6 different rankings derived from famous human-constructed resources such as WordNet, OntoNotes, Oxford, Wikipedia etc., for 6 different standard metrics. We also visualize and analyze the correlation between the human rankings. A valuable by-product of our method is the ability to sample, at no extra cost, sentences containing different senses of a given word. Finally, the fully unsupervised nature of our method makes it applicable to any language. Code and data are publicly available at https://github.com/ksipos/polysemy-assessment . The paper was accepted as a long paper at EACL 2021.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
