文章基本信息

标题：How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis
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作者：Ivan Sanchez ; Sebastian Riedel
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2017
卷号：2017
页码：401-407
语种：English
出版社：ACL Anthology
摘要：One key property of word embeddings currently under study is their capacity to encode hypernymy. Previous works have used supervised models to recover hypernymy structures from embeddings. However, the overall results do not clearly show how well we can recover such structures. We conduct the first dataset-centric analysis that shows how only the Baroni dataset provides consistent results. We empirically show that a possible reason for its good performance is its alignment to dimensions specific of hypernymy: generality and similarity.