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Record W2294102067

A Description of ZZ_INFO_TECH System at KBP 2013

2013· article· en· W2294102067 on OpenAlexvenueno aff
Jie Zhou, Yaoyi Xi

Bibliographic record

VenueTheory and applications of categories · 2013
Typearticle
Languageen
FieldComputer Science
TopicTopic Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceRanking (information retrieval)HeuristicTask (project management)Context (archaeology)Similarity (geometry)Filter (signal processing)Support vector machineData miningInformation retrievalPopulationTrainKnowledge baseSimple (philosophy)Artificial intelligenceMachine learningGeographyEngineering
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we report the participation of ZZ_INFO_TECH team in entity linking task for Knowledge Base Population at Text Analysis Conference 2013. Our team submitted 4 runs for this evaluation task. In our system, we adopts some simple heuristic rules and context similarity to filter irrelevant candidates; extracts multiple features and trains SVM Ranking model to realize candidate ranking; computes five common statistics and trains SVM model to identify NIL query.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.317

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0950.089

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 head0.217
Teacher spread0.204 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2013
Admission routes1
Has abstractyes

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