standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| second reading | alternatives | an independent re-reading of a facet, recorded whether or not it agrees |
| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| content tag | content_tags | a finding, definition, quantity, statistic or variable named in the paper |
| control | controls | an experiment run to eliminate a rival explanation |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| phenomenon | phenomenon | the effect or regularity under study |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| transfer | transfers | a method or concept carried in from another field |
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 1 relation here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| claim_type | method Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_type | the paper's words | |
| phenomenon | word vector representations from co-occurrence statistics Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | phenomenon | the paper's words | |
| method | system-or-tool-building Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: system-or-tool-building | method | the paper's words | |
| mechanisms | mechanisms Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | mechanisms[0] | the tagger's words | |
| limitations | the origin of the linguistic regularities captured by word-vector arithmetic was not understood Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the explanatory claim about vector arithmetic: Quotation withheld | limitations[0] | the tagger's words | |
| limitations | The chosen exponent in the weighting function is justified only empirically, not derived. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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 | limitations[1] | the tagger's words | |
| limitations | The head-to-head comparison with word2vec is confounded by many performance-affecting parameters. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the head-to-head comparison with word2vec: A RIGOROUS QUANTITATIVE COMPARISON ... IS COMPLICATED BY THE EXISTENCE OF MANY PARAMETERS THAT HAVE A STRONG EFFECT ON PERFORMANCE | limitations[2] | the tagger's words | |
| limitations | Unspecified parameters were left at defaults, a simplification the authors say a more thorough analysis should relax. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: every reported comparison, and it is the confound item 2 names: WE SET ANY UNSPECIFIED PARAMETERS TO THEIR DEFAULT VALUES, ASSUMING THAT THEY ARE CLOSE TO OPTIMAL, THOUGH WE ACKNOWLEDGE THAT THIS SIMPLIFICATION SHO[uld be noted] | limitations[3] | the tagger's words | |
| limitations | The largest-corpus (840B token) run used a different preprocessing, so its results are not comparable with the reported ones. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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] | limitations[4] | the tagger's words | |
| limitations | 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. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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 | limitations[5] | 1.0 | the tagger's words |
| limitations | The exponent that shapes the weighting function has empirical motivation only. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: results that depend on the weighting function: the authors state they offer only empirical motivation for the value 3/4, noting a similar fractional power scaling was found elsewhere | limitations[6] | 1.0 | the tagger's words |
| limitations | The distinction the model is built on is declared arbitrary by the authors. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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 | limitations[7] | 1.0 | the tagger's words |
| limitations | The largest corpus in the paper was processed differently, so its numbers do not sit on the same scale as the rest. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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 | limitations[8] | 1.0 | the tagger's words |
| limitations | The complexity result rests on an assumed distribution rather than a measured one. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. what this limits: the claim about how the model'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's frequency rank | limitations[9] | 1.0 | the tagger's words |
| transfers | global matrix factorization (from latent semantic analysis) combined with local context windows to learn word vectors Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | transfers[0] | the tagger's words | |
| controls | holding corpus, vocabulary size, vector dimension and context window fixed across GloVe and word2vec, and plotting accuracy against training time Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: that the accuracy difference between GloVe and word2vec comes from differing hyperparameters or training budget rather than from the model itself · outcome: partial | controls[0] | 0.8 | the tagger's words |
| concepts | word vector Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: word vector | concepts[4] | the paper's words | |
| concepts | context word Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: context word | concepts[5] | the paper's words | |
| concepts | word analogy Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: word analogy | concepts[6] | the paper's words | |
| concepts | context window Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: context window | concepts[7] | the paper's words | |
| concepts | syntactic subtask Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: syntactic subtask | concepts[8] | the paper's words | |
| senses | what the paper calls its own object - WORD VECTORS AND WORD REPRESENTATIONS, NEVER EMBEDDINGS: it uses those two terms 24 and 11 times; 'word embeddings' appears exactly once and describes another group's method rather than this paper's output, and 'distributional semantics' does not appear at all Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[0] | 1.0 | the tagger's words |
| senses | 'word embeddings' IN THIS PAPER - SOMEONE ELSE'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 Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[1] | 1.0 | the tagger's words |
| senses | 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 Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[2] | 1.0 | the tagger's words |
| senses | 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 Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[3] | 1.0 | the tagger's words |
| alternatives | claim_type: method Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. a second reading reaching the same value. Recorded: method — note: A BLIND second reading by the project on 2026-08-25: the record'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)., | alternatives.claim_type | 0.85 | the tagger's words |
| alternatives | method: system-or-tool-building Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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'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)., | alternatives.method | 0.8 | the tagger's words |
| alternatives | phenomenon: why linear regularities emerge in word vector spaces learned from co-occurrence statistics Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. a second reading DIFFERING from the recorded value. Recorded: word vector representations from co-occurrence statistics — note: A BLIND second reading by the project on 2026-08-25: the record'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)., | alternatives.phenomenon | 0.85 | the tagger's words |
| claim_relations | builds-on → global matrix factorization (LSA) and local context-window methods Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[0] | the tagger's words | |
| claim_relations | contradicts → Baroni et al. (2014) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[1] | the tagger's words | |
| claim_relations | extends → skip-gram and ivLBL window-based models Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[2] | the tagger's words | |
| concept_edges | context window —causes→ syntactic subtask Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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. also in: context window · syntactic subtask | concept_edges[1] | the tagger's words | |
| content_tags | 75% — accuracy on the word analogy task (GloVe headline result) Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.quantities[0] | the tagger's words | |
| content_tags | 6 billion — corpus size used to build the co-occurrence statistics Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.quantities[1] | the tagger's words | |
| content_tags | GloVe is a new global log-bilinear regression model combining the advantages of global matrix factorization and local context window methods. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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. | content_tags.findings[0] | the tagger's words | |
| content_tags | Only in the ratio of co-occurrence probabilities does noise from non-discriminative words cancel out. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[1] | the tagger's words | |
| content_tags | word-word co-occurrence counts Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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. also in: word-word co-occurrence counts | content_tags.variables[0] | the tagger's words | |
| content_tags | GloVe Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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. also in: GloVe | content_tags.definitions[0] | the tagger's words | |
| content_tags | word analogy task Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: word analogy task | content_tags.definitions[1] | the tagger's words | |
| content_tags | correlation Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: correlation | content_tags.statistics[0] | the tagger's words | |
| content_tags | regression Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. 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. also in: regression | content_tags.statistics[1] | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.