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Record W2013181170 · doi:10.1109/iri.2010.5558906

Sentence level fact based search engine: News Fact Finder

2010· article· en· W2013181170 on OpenAlex
Cristina Ribeiro, Ricardo Salmon, Swathi Amarala, Christina Hamada

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSubstringComputer scienceSearch engineSentenceInformation retrievalThe InternetMatching (statistics)SuffixOrder (exchange)World Wide WebArtificial intelligenceData structureMathematicsProgramming language

Abstract

fetched live from OpenAlex

Users searching the Internet for news are not able to find relevant fact-based results for certain queries using the major search engines. Queries that require exact substring matching in order to obtain very relevant results are not currently possible. Furthermore, search engines do not discriminate in returning results that are opinions and not quantifiable facts. Our sentence level search engine, News Fact Finder, is designed using suffix arrays, filters out opinions, and produces very relevant results that are attractive to users. The News Fact Finder produces a 73% success rate of providing relevant fact based results.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.280
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

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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