@inproceedings{aguirre-celis-miikkulainen-2021-understanding,
    title = "Understanding the Semantic Space: How Word Meanings Dynamically Adapt in the Context of a Sentence",
    author = "Aguirre-Celis, Nora  and
      Miikkulainen, Risto",
    editor = "Lewis, Martha  and
      Sadrzadeh, Mehrnoosh",
    booktitle = "Proceedings of the 2021 Workshop on Semantic Spaces at the Intersection of NLP, Physics, and Cognitive Science (SemSpace)",
    month = jun,
    year = "2021",
    address = "Groningen, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.semspace-1.1/",
    pages = "1--11",
    abstract = "How do people understand the meaning of the word ``small'' when used to describe a mosquito, a church, or a planet? While humans have a remarkable ability to form meanings by combining existing concepts, modeling this process is challenging. This paper addresses that challenge through CEREBRA (Context-dEpendent meaning REpresentations in the BRAin) neural network model. CEREBRA characterizes how word meanings dynamically adapt in the context of a sentence by decomposing sentence fMRI into words and words into embodied brain-based semantic features. It demonstrates that words in different contexts have different representations and the word meaning changes in a way that is meaningful to human subjects. CEREBRA{'}s context-based representations can potentially be used to make NLP applications more human-like."
}