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Record W2895358146 · doi:10.1145/3209280.3209527

Choosing Math Features for BM25 Ranking with Tangent-L

2018· article· en· W2895358146 on OpenAlexafffund
Dallas J. Fraser, Andrew W. Kane, Frank Wm. Tompa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Informatics
KeywordsRanking (information retrieval)NotationTangentComputer scienceTask (project management)Information retrievalAlgebra over a fieldArtificial intelligenceNatural language processingMathematicsPure mathematicsArithmeticGeometry

Abstract

fetched live from OpenAlex

Combining text and mathematics when searching in a corpus with extensive mathematical notation remains an open problem. Recent results for Tangent-3 on the math and text retrieval task at NTCIR-12, for example, have room for improvement, even though formula retrieval appeared to be fairly successful.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.018
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0090.006
Science and technology studies0.0020.001
Scholarly communication0.0040.004
Open science0.0040.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0170.027

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.248
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2018
Admission routes2
Has abstractyes

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