Benchmarks for Open Relation Extraction
Notice bibliographique
Résumé
* Introduction* Benchmarks for Open Relation Extraction was developed by the University of Alberta and contains annotations for approximately 14,000 sentences from The New York Times Annotated Corpus (LDC2008T19) and Treebank-3 (LDC99T42). This corpus was designed to contain benchmarks for the task of open relation extraction (ORE), along with sample extractions from ORE methods and evaluation scripts for computing a method's precision and recall. ORE attempts to extract as many relations as described in a corpus without relying on relation-specific training data. The traditional approach to relation extraction requires substantial training effort for each relation of interest. That can be unpractical for massive collections such as found on the web. Open relation extraction offers an alternative by extracting unseen relations as they come. It does not require training data for any particular relation, making it suitable for applications that require a large (or even unknown) number of relations. Results published in ORE literature are often not comparable due to the lack of reusable annotations and differences in evaluation methodology. The goal of this benchmark data set is to provide annotations that are flexible and can be used to evaluate a wide range of methods. *Data* Binary and n-ary relations were extracted from the text sources. Sentences were annotated for binary relations manually and automatically. In the manual sentence annotation, two entities and a trigger (a single token indicating a relation) were identified for the relation between them, if one existed. A window of tokens allowed to be in a relation was specified; that included modifiers of the trigger and prepositions connecting triggers to their arguments. For each sentence annotated with two entities, a system must extract a string representing the relation between them. The evaluation method deemed an extraction as correct if it contained the trigger and allowed tokens only. The automatic annotator identified pairs of entities and a trigger of the relation between them; the evaluation script for that experiment deemed an extraction correct if it contained the annotated trigger. For n-ary relations, sentences were annotated with one relation trigger and all of its arguments. An extracted argument was deemed correct if it was annotated in the sentence. This release also includes extractions from the following ORE methods: ReVerb, SONEX, OLLIE, PATTY, TreeKernel, SwiRL, Lund and EXEMPLAR. Evaluation scripts are also provided for computing a method's precision and recall. *Samples* Please view this sample. *Updates* None at this time.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,021 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».