Controlled Experiments for Word Embeddings
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Abstract (arXiv)
An experimental approach to studying the properties of word embeddings is proposed. Controlled experiments, achieved through modifications of the training corpus, permit the demonstration of direct relations between word properties and word vector direction and length. The approach is demonstrated using the word2vec CBOW model with experiments that independently vary word frequency and word co-occurrence noise. The experiments reveal that word vector length depends more or less linearly on both word frequency and the level of noise in the co-occurrence distribution of the word. The coefficients of linearity depend upon the word. The special point in feature space, defined by the (artificial) word with pure noise in its co-occurrence distribution, is found to be small but non-zero.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Oct 2015.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2021Effect of Post-processing on Contextualized Word Representationscited by 7
- 2024Importance-Aware Data Augmentation for Document-Level Neural Machine Translationcited by 4
- 2021Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semanticscited by 3
- 2023The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddingscited by 2
- 2026Semantic Gradients Interactions in SSD: A Case Study in Racial Identity and Hate Speechcited by 0
- 2026Semantic Gradients Interactions in SSD: A Case Study in Racial Identity and Hate Speech26 May 2026
- 2024Importance-Aware Data Augmentation for Document-Level Neural Machine Translation27 Jan 2024
- 2023The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings2 Jan 2023
- 2021Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics10 Nov 2021
- 2021Effect of Post-processing on Contextualized Word Representations15 Apr 2021