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While many attacked Searle\u2019s Chinese Room thought experiment, no one seemed to question this most basic assumption. For how can computers learn something that is not in the data? Emily Bender and Alexander Koller (2020) recently presented a related thought experiment\u2014the so-called Octopus thought experiment, which replaces the rule-based interlocutor of Searle\u2019s thought experiment with a neural language model. The Octopus thought experiment was awarded a best paper prize and was widely debated in the AI community. Again, however, even its fiercest opponents accepted the premise that what a word refers to cannot be induced in the absence of direct supervision. I will argue that what a word refers to <jats:italic>is<\/jats:italic>\u00a0probably learnable from raw text alone. Here\u2019s why: higher-order concept co-occurrence statistics are stable across languages and across modalities, because language use (universally) reflects the world we live in (which is relatively stable). Such statistics are sufficient to establish what words refer to. My conjecture is supported by a literature survey, a thought experiment, and an actual experiment.<\/jats:p>","DOI":"10.1007\/s11023-023-09622-4","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T18:01:15Z","timestamp":1674496875000},"page":"33-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Grounding the Vector Space of an Octopus: Word Meaning from Raw Text"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5250-4276","authenticated-orcid":false,"given":"Anders","family":"S\u00f8gaard","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"9622_CR1","doi-asserted-by":"crossref","unstructured":"Abdou, M., Kulmizev, A., Hershcovich, D., Frank, S., Pavlick, E., & S\u00f8gaard, A. (2021). 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