mlsanityData to 7 Oct 2026

BERT's Conceptual Cartography: Mapping the Landscapes of Meaning

arXiv 2408.07190 ↗First posted 13 Aug 2024cs.CLcited by 1

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Abstract (arXiv)

Conceptual Engineers want to make words better. However, they often underestimate how varied our usage of words is. In this paper, we take the first steps in exploring the contextual nuances of words by creating conceptual landscapes -- 2D surfaces representing the pragmatic usage of words -- that conceptual engineers can use to inform their projects. We use the spoken component of the British National Corpus and BERT to create contextualised word embeddings, and use Gaussian Mixture Models, a selection of metrics, and qualitative analysis to visualise and numerically represent lexical landscapes. Such an approach has not yet been used in the conceptual engineering literature and provides a detailed examination of how different words manifest in various contexts that is potentially useful to conceptual engineering projects. Our findings highlight the inherent complexity of conceptual engineering, revealing that each word exhibits a unique and intricate landscape. Conceptual Engineers cannot, therefore, use a one-size-fits-all approach when improving words -- a task that may be practically intractable at scale.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 57 references that are in the map. First posted on arXiv 13 Aug 2024.

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17 of 57 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
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  6. 2018Deep contextualized word representationscited by 2,285
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  8. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,041
  9. 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 770
  10. 2019Visualizing and Measuring the Geometry of BERTcited by 215
  11. 2019Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentencecited by 121
  12. 2020Analysing Lexical Semantic Change with Contextualised Word Representationscited by 92
5 more
  1. 2021Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Sensescited by 39
  2. 2024Attributions toward Artificial Agents in a modified Moral Turing Testcited by 18
  3. 2023Shades of meaning: Uncovering the geometry of ambiguous word representations through contextualised language modelscited by 2
  4. 2021Using Distributional Principles for the Semantic Study of Contextual Language Modelscited by 1
  5. 2023The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretrainingcited by 1

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