Revisiting Cosine Similarity via Normalized ICA-transformed Embeddings (2025.coling-main)
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| Challenge: | Existing studies on cosine similarity focus on the angle or correlation coefficient, but this study proposes a novel interpretation of the term word similarity. |
| Approach: | They propose a method for selecting statistically significant axes by deriving the probability distributions that govern each component and the product of components. |
| Outcome: | The proposed interpretation of cosine similarity is demonstrated through intuitive numerical examples and thorough numerical experiments. |
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| Challenge: | Existing studies have shown that ICA can reveal universal semantic axes across languages but lack verification of consistency of independent components within and across languages. |
| Approach: | They propose to use independent component analysis to identify independent components that are more interpretable than PCA to find universal semantic axes. |
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Understanding Higher-Order Correlations Among Semantic Components in Embeddings (2024.emnlp-main)
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| Challenge: | Independent Component Analysis (ICA) is an effective method for visualizing and interpreting the geometric structure of embeddings. |
| Approach: | They quantified embeddings' non-independencies using higher-order correlations and a maximum spanning tree of semantic components. |
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| Challenge: | Embedding is an important component in natural language processing, but interpreting high-dimensional embeddings remains challenging. |
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Discovering Universal Geometry in Embeddings with ICA (2023.emnlp-main)
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| Challenge: | Existing studies have focused on achieving sparse embeddings or acquiring semantic axes, but this study focuses on the intrinsic independence present within embeddables. |
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Correlation Coefficients and Semantic Textual Similarity (N19-1)
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| Challenge: | Existing research into semantic textual similarity has focused on word embeddings . little attention has been devoted to similarity measures between word embeds - a new study shows . |
| Approach: | They show that cosine similarity is essentially equivalent to the Pearson correlation coefficient for all common word vectors. |
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Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing (N19-1)
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| Challenge: | Existing methods to estimate document similarity based on word embeddings are mediocre . a recent study compared word and sentence embedded documents to a similarity estimate using matrix norms. |
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Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
| Approach: | They propose a framework for interpretable text embeddings and text similarity explanation . they characterize the main ideas, approaches, and trade-offs and discuss lessons learned . |
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Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words (2022.acl-short)
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| Challenge: | We find that word similarities estimated by cosine over contextual embeddings are understated and trace this effect to training data frequency. |
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A Rank-Based Similarity Metric for Word Embeddings (P18-2)
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| Challenge: | Word Embeddings have become a standard for word representations, with vector cosine being the only similarity metric. |
| Approach: | They propose to use rank-based similarity estimation metrics to measure word similarity . they find WE outperforms vector cosine in the recent outlier detection task . |
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Exploring Interpretability of Independent Components of Word Embeddings with Automated Word Intruder Test (2024.lrec-main)
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| Challenge: | Independent Component Analysis (ICA) is an algorithm for finding separate sources in a mixed signal. |
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