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Distributional Structure
Abstract
For the purposes of the present discussion, the term structure will be used in the following non-rigorous sense: A set of phonemes or a set of data is structured in respect to some feature, to the extent that we can form in terms of that feature some organized system of statements which describes the members of the set and their interrelations (at least up to some limit of complexity). In this sense, language can be structured in respect to various independent features. And whether it is structured (to more than a trivial extent) in respect to, say, regular historical change, social intercourse, meaning, or distribution - or to what extent it is structured in any of these respects - is a matter decidable by investigation. Here we will discuss how each language can be described in terms of a distributional structure, i.e. in terms of the occurrence of parts (ultimately sounds) relative to other parts, and how this description is complete without intrusion of other features such as history or meaning. It goes without saying that other studies of language - historical, psychological, etc.-are also possible, both in relation to distributional structure and independently of it.
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“In LLMs, semantic similarity is determined based on distribution across contexts: words that appear in similar contexts are mapped to nearby vectors.”
From The Vector Grounding Problem (2026) · cites this paper as Harris, 1954
In the citing paper
“There is a deep connection between the way in which LLMs learn embeddings from linguistic corpora, and the old idea from structural linguistics according to which word meaning can be inferred from the contexts in which they appear, known as the distributional hypothesis (Firth, 1957;Harris, 1954).”
From The Vector Grounding Problem (2026) · cites this paper as Harris, 1954
In the citing paper
“This result mirrors a relentless trend in DL for various domains, where learned features ultimately outperform and displace long-used expert features, such as the scale-invariant feature transform (SIFT) [8] and Bag-of-words [9].”
From An Introduction to Deep Learning for the Physical Layer (2017) · cites this paper as [9]
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Metadata
- Authors
- Harris, Z. S.
- Corresponding author
- Harris, Zellig S.
- Venue
- WORD
- Publisher
- Informa UK Limited
- Published
- 1954-08
- Volume
- 10
- Issue
- 2-3
- Pages
- 146-162
- ISSN
- 0043-7956 (ISSN-L)
- 2373-5112
- OpenAlex
- W2882319491
- Cited by
- 4,807
- References
- 0
- Citations by year
- 2026: 181 · 2025: 217 · 2024: 303 · 2023: 371 · 2022: 365 · 2021: 428 · 2020: 448 · 2019: 474 · 2018: 373 · 2017: 324 · 2016: 279 · 2015: 242 · 2014: 175 · 2013: 159 · 2012: 92
- FWCI
- 16.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- WORD (publishedVersion)
- Topics
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Social Sciences › Arts and Humanities › Language and Linguistics › Linguistics and Language Studies
- Social Sciences › Arts and Humanities › Language and Linguistics › Historical Linguistics and Language Studies
- Keywords
- phonemes, phonological features, phonological description, sound distribution, language structure, historical change, meaning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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