Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations (C18-1)
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| Challenge: | Neural activation models have been proposed to map word semantics to localized neural activations. |
| Approach: | They propose a computational model that estimates semantic similarity in the neural activation space and investigate its performance for various natural language processing tasks. |
| Outcome: | The proposed model performs better than state-of-the-art word embeddings for the task of semantic similarity estimation between very similar or very dissimilar words while performing well on other tasks such as entailment and word categorization. |
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| Challenge: | Existing and novel similarity measures are used to analyze contextual word representations . different architectures have rather similar representations, but different individual neurons. |
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Mapping Brains with Language Models: A Survey (2023.findings-acl)
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| Challenge: | accumulated evidence for brain and language model activations remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism. |
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Analogy Models for Neural Word Inflection (2020.coling-main)
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| Challenge: | Neural network models are usually very data-hungry and performance of such models can suffer when labeled data is not available. |
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Lexicosyntactic Inference in Neural Models (D18-1)
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| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
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Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution (2020.coling-main)
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| Challenge: | Lexical substitution is a powerful technology used in various NLP applications . it generates plausible words that can replace a given word in a textual context . |
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Implicit Representations of Meaning in Neural Language Models (2021.acl-long)
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| Challenge: | Neural language models (NLMs) encode lexical relations and syntactic structure, but their effectiveness is still unclear. |
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Lexical Semantics with Large Language Models: A Case Study of English “break” (2023.findings-eacl)
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| Challenge: | Large neural language models (LLMs) can be powerful tools for research in lexical semantics. |
| Approach: | They argue that large neural language models can be powerful tools for research in lexical semantics by capturing known sense distinctions and identifying informative new sense combinations. |
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Lexical Relation Mining in Neural Word Embeddings (2020.coling-main)
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| Challenge: | Conventionally, lexical relations in word vector space have been defined by collections of relatively consistent relationships, or vector offsets, between word-pairs. |
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Towards Explainable Evaluation of Language Models on the Semantic Similarity of Visual Concepts (2022.coling-1)
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Maria Lymperaiou, George Manoliadis, Orfeas Menis Mastromichalakis, Edmund G. Dervakos, Giorgos Stamou
| Challenge: | Recent advances in NLP research have focused on robustness and explainability issues of their evaluation strategies. |
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Evaluating Lexical Proficiency in Neural Language Models (2025.acl-long)
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| Challenge: | Recent advances in Natural Language Processing have been significantly shaped by the Deep Learning tsunami and the introduction of Transformer-based Language Models. |
| Approach: | They validated a framework to assess the lexical proficiency and linguistic creativity of Transformer-based Language Models (LMs) by analyzing performance of LMs of different sizes across tasks involving the generation, definition, and contextual usage of lexicals, neologisms, and nonce words. |
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