{"total":25,"rows":[{"id":"2307.00162","title":"What Do Self-Supervised Speech Models Know About Words?","created":"2023-06-30","cited_by_count":64,"area":null},{"id":"2406.11565","title":"Extrinsic Evaluation of Cultural Competence in Large Language Models","created":"2024-06-17","cited_by_count":24,"area":null},{"id":"2203.02679","title":"Just Rank: Rethinking Evaluation with Word and Sentence Similarities","created":"2022-03-05","cited_by_count":20,"area":null},{"id":"2106.04302","title":"Obtaining Better Static Word Embeddings Using Contextual Embedding Models","created":"2021-06-08","cited_by_count":18,"area":null},{"id":"2203.07860","title":"Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost","created":"2022-03-15","cited_by_count":9,"area":null},{"id":"2101.08700","title":"Multi-sense embeddings through a word sense disambiguation process","created":"2021-01-21","cited_by_count":7,"area":null},{"id":"2310.10262","title":"Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings","created":"2023-10-16","cited_by_count":6,"area":null},{"id":"2107.10413","title":"Theoretical foundations and limits of word embeddings: what types of meaning can they capture?","created":"2021-07-22","cited_by_count":5,"area":null},{"id":"2204.12386","title":"Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings","created":"2022-04-26","cited_by_count":5,"area":null},{"id":"2211.03634","title":"No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media","created":"2022-11-07","cited_by_count":5,"area":null},{"id":"2502.05331","title":"Fine-Tuned LLMs are \"Time Capsules\" for Tracking Societal Bias Through Books","created":"2025-02-07","cited_by_count":5,"area":null},{"id":"2203.04366","title":"It's AI Match: A Two-Step Approach for Schema Matching Using Embeddings","created":"2022-03-08","cited_by_count":4,"area":null},{"id":"2305.07303","title":"Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions","created":"2023-05-12","cited_by_count":4,"area":null},{"id":"2310.15905","title":"Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces","created":"2023-10-24","cited_by_count":2,"area":null},{"id":"2405.07778","title":"A Comprehensive Analysis of Static Word Embeddings for Turkish","created":"2024-05-13","cited_by_count":2,"area":null},{"id":"2104.05228","title":"SuperSim: a test set for word similarity and relatedness in Swedish","created":"2021-04-12","cited_by_count":1,"area":null},{"id":"2507.05933","title":"Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2510.01652","title":"Learning to Look at the Other Side: A Semantic Probing Study of Word Embeddings in LLMs with Enabled Bidirectional Attention","created":"2025-10-02","cited_by_count":1,"area":null},{"id":"2102.10437","title":"Understanding and Enhancing the Use of Context for Machine Translation","created":"2021-02-20","cited_by_count":0,"area":null},{"id":"2103.03108","title":"An empirical analysis of phrase-based and neural machine translation","created":"2021-03-04","cited_by_count":0,"area":null},{"id":"2108.09529","title":"Term Interrelations and Trends in Software Engineering","created":"2021-08-21","cited_by_count":0,"area":null},{"id":"2210.09972","title":"On the Information Content of Predictions in Word Analogy Tests","created":"2022-10-18","cited_by_count":0,"area":null},{"id":"2407.12342","title":"Word Embedding Dimension Reduction via Weakly-Supervised Feature Selection","created":"2024-07-17","cited_by_count":0,"area":null},{"id":"2411.08384","title":"Interpretable Syntactic Representations Enable Hierarchical Word Vectors","created":"2024-11-13","cited_by_count":0,"area":null},{"id":"2502.20975","title":"Set-Theoretic Compositionality of Sentence Embeddings","created":"2025-02-28","cited_by_count":0,"area":null}],"next_cursor":null}