Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Computational neuroscience

Understanding the combined meaning of words

Nature Computational Science volume 2, pages 701–702 (2022)Cite this article

Subjects

Recent work uses a language model to gain insight into how the human brain understands the combined meaning of words in a sentence, and uncovers parts of the brain that contribute to this understanding.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The meaning of a larger phrase is more than the sum of its words.

References

  1. Toneva, M., Mitchell, T. M. & Wehbe, L. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00354-6 (2022).

  2. Bommasani, R. et al. Preprint at https://arxiv.org/abs/2108.07258 (2021).

  3. Peters, M. et al. Deep contextualized word representations. In Proc. 2018 Conference of the North American Chapter of the Association for Computational Linguistics 2227–2237 (Association for Computational Linguistics, 2018).

  4. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics 4171–4186 (Association for Computational Linguistics, 2019).

  5. Radford, A. et al. OpenAI blog 1, 9 (2019).

    Google Scholar 

  6. Vulić, I., Ponti, E. M., Litschko. R., Glavaš, G. & Korhonen, A. Probing pretrained language models for lexical semantics. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing 7222–7240 (Association for Computational Linguistics, 2020).

  7. Linzen, T., Dupoux, E. & Goldberg, Y. Trans. Assoc. Comput. Linguist. 4, 521–535 (2016).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Texas at Austin, Austin, TX, USA

    Katrin Erk

Authors
  1. Katrin Erk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katrin Erk.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Erk, K. Understanding the combined meaning of words. Nat Comput Sci 2, 701–702 (2022). https://doi.org/10.1038/s43588-022-00338-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-022-00338-6

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics