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        <title>GloVe: Global Vectors for Word Representation</title>
        <author>Jeffrey Pennington</author>
        <author>Richard Socher</author>
        <author>Christopher D. Manning</author>
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          <title>GloVe: Global Vectors for Word Representation</title>
          <date>2014</date>
          <publisher>EMNLP</publisher>
          <idno type="DOI">10.3115/v1/D14-1162</idno>
          
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>word vector representations from co-occurrence statistics</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>system-or-tool-building</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <term>learn word vectors from the global co-occurrence matrix via a log-bilinear model on ratios of co-occurrence probabilities, so vector differences encode meaning</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>the origin of the linguistic regularities captured by word-vector arithmetic was not understood</label>
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        <label>The chosen exponent in the weighting function is justified only empirically, not derived.</label>
        <note>what this limits: every result produced with the weighting function: WE OFFER ONLY EMPIRICAL MOTIVATION FOR CHOOSING THE VALUE 3/4 - the exponent is fitted, not derived</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The head-to-head comparison with word2vec is confounded by many performance-affecting parameters.</label>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The largest-corpus (840B token) run used a different preprocessing, so its results are not comparable with the reported ones.</label>
        <note>what this limits: the scalability demonstration specifically: TO DEMONSTRATE THE SCALABILITY OF THE MODEL, WE ALSO TRAINED IT ON A MUCH LARGER SIXTH CORPUS, CONTAINING 840 BILLION TOKENS OF WEB DATA, BUT IN THIS [case the conditions differ]</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t9" type="limitations" ana="#limitations[5]" resp="#tagger" cert="1.0">
        <label>The comparison that establishes the headline result runs the competing method at default settings, assumed near-optimal, and the paper says the assumption should be relaxed.</label>
        <note>what this limits: the claim that this model outperforms word2vec: unspecified parameters of the compared systems were left at default values on the assumption they are close to optimal, which the authors call a simplification that a more thorough analysis should relax</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The exponent that shapes the weighting function has empirical motivation only.</label>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The distinction the model is built on is declared arbitrary by the authors.</label>
        <note>what this limits: any interpretation of the two vector sets as different kinds of thing: the paper states Quotation withheld and that the two roles may be exchanged</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The largest corpus in the paper was processed differently, so its numbers do not sit on the same scale as the rest.</label>
        <note>what this limits: a comparison across the corpora reported here: for the 840-billion-token web corpus the vocabulary was not lowercased, and the authors state the results are therefore not directly comparable</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>The complexity result rests on an assumed distribution rather than a measured one.</label>
        <note>what this limits: the claim about how the model&#x27;s cost scales: it requires assuming the number of co-occurrences of two words can be modelled as a power-law function of that pair&#x27;s frequency rank</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>global matrix factorization (from latent semantic analysis) combined with local context windows to learn word vectors</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t15" type="controls" ana="#controls[0]" resp="#tagger" cert="0.8">
        <label>holding corpus, vocabulary size, vector dimension and context window fixed across GloVe and word2vec, and plotting accuracy against training time</label>
        <note>eliminates: that the accuracy difference between GloVe and word2vec comes from differing hyperparameters or training budget rather than from the model itself · outcome: partial</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>word vector</label>
        <term>word vector</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>context word</label>
        <term>context word</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>what the paper calls its own object - WORD VECTORS AND WORD REPRESENTATIONS, NEVER EMBEDDINGS: it uses those two terms 24 and 11 times; &#x27;word embeddings&#x27; appears exactly once and describes another group&#x27;s method rather than this paper&#x27;s output, and &#x27;distributional semantics&#x27; does not appear at all</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t22" type="senses" ana="#senses[1]" resp="#tagger" cert="1.0">
        <label>&#x27;word embeddings&#x27; IN THIS PAPER - SOMEONE ELSE&#x27;S OBJECT: the single occurrence attributes the term to Levy et al., alongside vLBL and ivLBL, as a rival PPMI-based approach; the authors never apply the word to what they themselves produce</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t23" type="senses" ana="#senses[2]" resp="#tagger" cert="1.0">
        <label>the analogy evaluation - AS DIMENSIONS OF DIFFERENCE, NOT DISTANCE: it probes the vector space by examining the various dimensions of difference between vectors rather than the scalar distance between them, which is why a model can win on similarity and lose on analogy</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>the context window - SYMMETRIC OR ASYMMETRIC, AND THE CHOICE MOVES ONE SUBTASK ONLY: a window extending both ways is called symmetric and one extending only left asymmetric, and the syntactic subtask specifically improves with small asymmetric windows</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>claim_type: method</label>
        <note>a second reading reaching the same value. Recorded: method — note: A BLIND second reading by the project on 2026-08-25: the record&#x27;s own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is).,</note>
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        <label>method: system-or-tool-building</label>
        <note>a second reading reaching the same value. Recorded: system-or-tool-building — note: A BLIND second reading by the project on 2026-08-25: the record&#x27;s own claim type/method/phenomenon were not looked at until every value here was written and substring-verified. Not applied - a person decides. Sampling frame: the 504 staged records carrying no alternatives block, so this never re-measures the 202. All 11 records with 130+ tags were taken as a census and 19 more spread one per discipline across 22 disciplines - the dense non-neuroscience cell is the one the 202 do not contain (inside them, 94-100% of the densest bands are neuroscience; among these 11, one is).,</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>builds-on → global matrix factorization (LSA) and local context-window methods</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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      <span xml:id="t29" type="claim_relations" ana="#claim_relations[1]" resp="#tagger">
        <label>contradicts → Baroni et al. (2014)</label>
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        <label>extends → skip-gram and ivLBL window-based models</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>context window —causes→ syntactic subtask</label>
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        <note>strength of the relation: 0.8
Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says performance on the syntactic subtask is better for small context windows; causes is loose.</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>75% — accuracy on the word analogy task (GloVe headline result)</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>6 billion — corpus size used to build the co-occurrence statistics</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>GloVe is a new global log-bilinear regression model combining the advantages of global matrix factorization and local context window methods.</label>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>Only in the ratio of co-occurrence probabilities does noise from non-discriminative words cancel out.</label>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>word-word co-occurrence counts</label>
        <term>word-word co-occurrence counts</term>
        <note>about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted.</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>GloVe</label>
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        <note>about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted.</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>correlation</label>
        <term>correlation</term>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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        <label>regression</label>
        <term>regression</term>
        <note>about this quote: QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted.</note>
        <note type="quotation-withheld">Quotation withheld because this record is not cleared for redistribution.</note>
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