Dense word embeddings, which encode semantic meanings of words to low dimensional vector spaces have become very popular in natural language processing (NLP) research due to their state-of-the-art performances in many NLP tasks. Word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces. However, in many cases, this semantic structure is broadly and heterogeneously distributed across the embedding dimensions, which makes interpretation a big challenge. In this study, we propose a statistical method to uncover the latent semantic structure in the dense word embeddings. To perform our analysis we introduce a new dataset (SEMCAT) that contains more than 6500 words semantically grouped under 110 categories. We further propose a method to quantify the interpretability of the word embeddings; the proposed method is a practical alternative to the classical word intrusion test that requires human intervention.

该研究提出了一种统计方法来揭示密集词嵌入中的潜在语义结构，并引入了一个新的数据集（SEMCAT），其中包含超过6500个在110个类别下语义分组的单词。研究还提出了一种量化词嵌入可解释性的方法，这是一种实用的替代方法，不需要人为干预。

词嵌入的语义结构和可解释性