An Attribution Relations Corpus for Political News
Bibliographic record
Abstract
An attribution occurs when an author quotes, paraphrases, or describes the statements and private states of a third party.Journalists use attribution to report statements and attitudes of public figures, organizations, and ordinary individuals.Properly recognizing attributions in context is an essential aspect of natural language understanding and implicated in many NLP tasks, but current resources are limited in size and completeness.We introduce the Political News Attribution Relations Corpus 2016 (PolNeAR) 2 -the largest, most complete attribution relations corpus to date.This dataset greatly increases the volume of high-quality attribution annotations, addresses shortcomings of existing resources, and expands the diversity of publishers sourced.PolNeAR is built on news articles covering the political candidates during the year leading up to US Presidential Election in November of 2016.The dataset will support the creation of sophisticated end-to-end solutions for attribution extraction and invite interdisciplinary collaboration between the NLP, communications, political science, and journalism communities.Along with the dataset we contribute revised guidelines aimed at improving clarity and consistency in the annotation task, and an annotation interface specially adapted to the task, for reproduction or extension of this work 2 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".