| Challenge: | Lexical ambiguity is widespread in language, allowing for the reuse of economical word forms and thus making language more efficient. |
| Approach: | They propose two ways to estimate lexical ambiguity as the entropy of meanings a word can take . they validate this hypothesis by using WordNet and BERT . |
| Outcome: | The proposed method shows that on six high-resource languages, there are significant correlations between the estimate and the number of synonyms a word has in WordNet. |
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| Challenge: | lexical ambiguity is a problem for NLP, but few tasks evaluate its impact on human intuitions. |
| Approach: | They propose to use contextualized word embeddings to evaluate word meaning . they use a dataset of human relatedness judgments and human estimates of sense dominance . |
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Putting Words in Context: LSTM Language Models and Lexical Ambiguity (P19-1)
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| Challenge: | In language, a word can contribute a very different meaning depending on the context . lexical ambiguity involves both morphosyntactic and semantic aspects . |
| Approach: | They propose a method to probe hidden representations for lexical and contextual information about words. |
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Evaluating Contextualized Representations of (Spanish) Ambiguous Words: A New Lexical Resource and Empirical Analysis (2025.naacl-long)
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| Challenge: | Few studies have systematically compared LMs’ contextualized word embeddings for languages beyond English. |
| Approach: | They evaluate Spanish ambiguous nouns in context in a suite of Spanish-language monolingual and multilingual BERT-based models. |
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Exploring BERT’s Sensitivity to Lexical Cues using Tests from Semantic Priming (2020.findings-emnlp)
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| Challenge: | Using English lexical stimuli, we find that BERT models show "priming" predicting a word with greater probability when the context includes a related word versus an unrelated one. |
| Approach: | They analyze a pre-trained BERT model with tests informed by semantic priming . they find that BERT too shows "priming" predicting a word with greater probability when context includes a related word versus an unrelated one. |
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Measuring Context-Word Biases in Lexical Semantic Datasets (2022.emnlp-main)
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| Challenge: | Existing pretrained contextualized models have been used to evaluate word-in-context representations in many lexical semantic tasks. |
| Approach: | They propose to quantify the degree of context or word biases in existing datasets by probing masked input. |
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Why do objects have many names? A study on word informativeness in language use and lexical systems (2024.emnlp-main)
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| Challenge: | lexical systems contain many different words that can be assigned to the same object . studies on language use have explored how speakers adapt their referring expressions to communicate in context without tackling in-context communication. |
| Approach: | They propose a simple measure of informativeness for words and lexical systems, grounded in a visual space, and analyze color naming data for English and Mandarin Chinese. |
| Outcome: | The proposed system allows for a soft mapping between referents and words, taking into account both in-context communication and the structure of the lexical system. |
How (Non-)Optimal is the Lexicon? (2021.naacl-main)
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| Challenge: | lexical meanings are mapped to wordforms by usage pressures and constraints on sequences of symbols. |
| Approach: | They propose a coding-theoretic view of the lexicon and a novel generative statistical model to quantify its compressibility under various constraints. |
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Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy (2021.acl-long)
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| Challenge: | Existing models that represent different senses of words in context are not accurate for polysemous words. |
| Approach: | They propose a multilingual dataset that evaluates the ability of models to accurately represent different lexical-semantic relations such as homonymy and synonymy. |
| Outcome: | The proposed models can disambiguate homonyms in context, but fail to represent words with different senses when occurring in similar sentences. |
We’re Afraid Language Models Aren’t Modeling Ambiguity (2023.emnlp-main)
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Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, Yejin Choi
| Challenge: | Ambiguity is an intrinsic feature of natural language, allowing us to anticipate misunderstandings and revise our interpretations as listeners. |
| Approach: | They use AmbiEnt to capture ambiguity in a sentence and analyze it to evaluate pretrained LMs. |
| Outcome: | The proposed model can flag political claims in the wild that are misleading due to ambiguity. |
Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness (2020.coling-main)
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| Challenge: | lexical semantics theories advocate that the meaning of words is represented as an inference graph, a feature mapping or a cooccurrence vector. |
| Approach: | They propose to probe different lexical semantics theories for their levels of cognitive plausibility and technological usefulness. |
| Outcome: | The proposed approach is superior to the other approaches, the authors argue . they show that the feature-based approach is more useful and more plausible than the other ones . |