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题名:	MSD-1030: A Well-built Multi-Sense Evaluation Dataset for Sense Representation Models
作者:	黃瀚萱 Huang, Hen-Hsen Yen, Ting-Yu Lee, Yang-Yin Shiue, Yow-Ting Shiue, Yow-Ting
贡献者:	資科系
关键词:	semantics;evaluation methodologies;crowdsourcing
日期:	2020-05
上传时间:	2021-06-04 14:42:41 (UTC+8)
摘要:	Sense embedding models handle polysemy by giving each distinct meaning of a word form a separate representation. They are considered improvements over word models, and their effectiveness is usually judged with benchmarks such as semantic similarity datasets. However, most of these datasets are not designed for evaluating sense embeddings. In this research, we show that there are at least six concerns about evaluating sense embeddings with existing benchmark datasets, including the large proportions of single-sense words and the unexpected inferior performance of several multi-sense models to their single-sense counterparts. These observations call into serious question whether evaluations based on these datasets can reflect the sense model’s ability to capture different meanings. To address the issues, we propose the Multi-Sense Dataset (MSD-1030), which contains a high ratio of multi-sense word pairs. A series of analyses and experiments show that MSD-1030 serves as a more reliable benchmark for sense embeddings. The dataset is available at http://nlg.csie.ntu.edu.tw/nlpresource/MSD-1030/.
關聯:	Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), European Language Resources Association, pp.5802-5809
数据类型:	conference
显示于类别:	[資訊科學系] 會議論文

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