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DWUG：一个涵盖四种语言的历时词用法图大型资源库

DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages

University of Stuttgart（斯图加特大学）；University of Gothenburg（哥德堡大学）；University of Cambridge（剑桥大学）；King’s College London（伦敦国王学院）；The Alan Turing Institute（阿兰·图灵研究所）

Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray

备注：
Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, and Barbara McGillivray. 2021. DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7079--7091, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics
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AI总结：

本研究构建了覆盖四种语言、基于10万条人工语义判断的DWUG大型历时词用法图标注资源，详述其多轮增量标注流程与义项聚类方案，为共时和历时词义研究提供支撑。

摘要：

众所周知，无论是共时层面还是历时层面，词义都极难捕捉。本文介绍了目前规模最大的分级语境化历时词义标注资源的构建过程，该资源覆盖四种语言，基于10万条人工语义接近度判断数据构建。文中详细阐述了多轮增量式标注流程、用于将用法聚类为义项的聚类算法选择，以及该数据集在历时和共时研究中的潜在应用场景。

英文摘要：

Word meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments. We thoroughly describe the multi-round incremental annotation process, the choice for a clustering algorithm to group usages into senses, and possible - diachronic and synchronic - uses for this dataset.

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。

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