# Patterns of Lexical Ambiguity in Contextualised Language Models

arXiv: https://arxiv.org/abs/2109.13032 · HTML: https://mlsanity.com/paper/2109.13032/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 56 references that are in the map.
- First posted on arXiv 27 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://mlsanity.com/paper/1808.09121/) (2018)
- [Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders](https://mlsanity.com/paper/2005.02590/) (2020)
- [Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings](https://mlsanity.com/paper/1909.10430/) (2019)
- [Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation](https://mlsanity.com/paper/1906.10007/) (2019)
- [Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Senses](https://mlsanity.com/paper/2104.14694/) (2021)
- [Contextualized Word Embeddings Encode Aspects of Human-Like Word Sense Knowledge](https://mlsanity.com/paper/2010.13057/) (2020)
- [CoSimLex: A Resource for Evaluating Graded Word Similarity in Context](https://mlsanity.com/paper/1912.05320/) (2019)
- [RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)](https://mlsanity.com/paper/2105.13266/) (2021)

## Built on by (most cited first, 4 of 4)

- [Revisiting Word Embeddings in the LLM Era](https://mlsanity.com/paper/2402.11094/) (2024)
- [Start Making Sense(s): A Developmental Probe of Attention Specialization Using Lexical Ambiguity](https://mlsanity.com/paper/2511.21974/) (2025)
- [Construction Grammar and Language Models](https://mlsanity.com/paper/2308.13315/) (2023)
- [Revisiting Word Embeddings in the LLM Era](https://mlsanity.com/paper/2502.19607/) (2025)

## Abstract (arXiv)

One of the central aspects of contextualised language models is that they should be able to distinguish the meaning of lexically ambiguous words by their contexts. In this paper we investigate the extent to which the contextualised embeddings of word forms that display multiplicity of sense reflect traditional distinctions of polysemy and homonymy. To this end, we introduce an extended, human-annotated dataset of graded word sense similarity and co-predication acceptability, and evaluate how well the similarity of embeddings predicts similarity in meaning. Both types of human judgements indicate that the similarity of polysemic interpretations falls in a continuum between identity of meaning and homonymy. However, we also observe significant differences within the similarity ratings of polysemes, forming consistent patterns for different types of polysemic sense alternation. Our dataset thus appears to capture a substantial part of the complexity of lexical ambiguity, and can provide a realistic test bed for contextualised embeddings. Among the tested models, BERT Large shows the strongest correlation with the collected word sense similarity ratings, but struggles to consistently replicate the observed similarity patterns. When clustering ambiguous word forms based on their embeddings, the model displays high confidence in discerning homonyms and some types of polysemic alternations, but consistently fails for others.