Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks (2023.eacl-main)
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| Challenge: | Recent work on word embeddings reports low correlations with human ratings . contextual language models (CLMs) have been successful in acquiring semantic and world knowledge. |
| Approach: | They propose to use BERT to probe contextual language models for predicting typicality scores. |
| Outcome: | The proposed methods improve on previous studies on word embeddings and their ability to predict typicality scores. |
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| Challenge: | a recent study has focused on homonymy, a variety of multiplicity of meanings exemplified by word forms with unrelated meanings. |
| Approach: | They investigate the extent to which contextualised embeddings reflect traditional distinctions of polysemy and homonymy. |
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Evaluating Word Embeddings with Categorical Modularity (2021.findings-acl)
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| Challenge: | Existing word embeddings use different bilingual supervision signals with varying levels of strength. |
| Approach: | They propose a graph modularity metric to measure word embedding quality . they use a set of 500 words belonging to 59 neurobiologically motivated semantic categories . |
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Obtaining Better Static Word Embeddings Using Contextual Embedding Models (2021.acl-long)
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| Challenge: | Recent contextual word embeddings have prohibitively high computational cost in many use-cases and are hard to interpret. |
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How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions (P19-1)
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| Challenge: | Cross-lingual word embeddings (CLEs) are used for downstream NLP tasks . CLEs are based on bilingual lexicon induction (BLI) evaluations vary greatly, hindering ability to interpret performance and properties of different CLE models. |
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Exploring the Value of Personalized Word Embeddings (2020.coling-main)
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| Challenge: | a subset of words belonging to specific psycholinguistic categories vary more in their representations across users . combining generic and personalized word embeddings yields the best performance . |
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Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (P19-1)
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| Challenge: | Contextual embeddings address the problem of meaning conflation hampering word embeddables. |
| Approach: | They propose a method that creates sense-level embeddings with full-coverage of WordNet without recourse to explicit sense distributions or task-specific modelling. |
| Outcome: | The proposed method surpasses previous systems using powerful models and is robust when ignoring part-of-speech and lemma features. |
Exploring Layer-wise Representations of English and Chinese Homonymy in Pre-trained Language Models (2025.findings-acl)
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| Challenge: | lexical ambiguity can arise due to the misunderstanding of its multiple senses. |
| Approach: | They propose to use part of speech to examine homonyms in Chinese and English . they find no universal layer depth excels in differentiating homnomial representations . |
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A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change (2024.naacl-long)
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| Challenge: | Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC) current evaluations focus on a specific task known as Graded Change Detection (GCD) however, performance comparisons between different approaches are often misleading due to diverse settings. |
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AMenDeD: Modelling Concepts by Aligning Mentions, Definitions and Decontextualised Embeddings (2024.lrec-main)
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| Challenge: | Contextualised Language Models (LMs) improve on word embeddings by encoding meaning of words in context. |
| Approach: | They propose to learn a unified embedding space in which all three types of representations can be integrated. |
| Outcome: | The proposed model outperforms existing approaches in ontology completion tasks. |
Static Embeddings as Efficient Knowledge Bases? (2021.naacl-main)
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| Challenge: | Recent research investigates factual knowledge stored in large pretrained language models . masked sentences such as “Paris is the capital of [MASK]” are used as probes . |
| Approach: | They use masked sentences to test whether a language model can capture factual knowledge . they show that static embeddings perform better than PLMs when restricted to a candidate set . |
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