Domino: SAIC's English Entity-Linking System.
Notice bibliographique
Résumé
The Domino system was SAIC’s student-intern entry to the English Entity-Linking track of the 2012 TAC-KBP competition. This paper describes how Domino was developed using components from the CUNY-BLENDER system and discusses the features and rules that were added to Domino. It analyzes Domino’s performance, and suggests ways in which we plan to improve the system in the future. 1.Building the Domino Baseline System 1.1 Motivation and Constraints Entity linking is a central task that analysts in the intelligence community (IC) often perform. Analysts must try to determine, for example, whether a person who is referred to in an intercepted email is the same as a person who is reported in some news article to have engaged in some terrorist activity. SAIC, which supports IC analysts in many different ways, is interested in developing methods to help automate the entitylinking process. There are many similarities between the IC entity-linking task and the TAC-KBP EntityLinking track, which focuses on linking named entities in news articles or blog posts with Wikipedia articles. We --a group of students from the University of Maryland who spent the summer of 2012 at SAIC, and our supervisor at SAIC --therefore decided, in mid-June 2012, to enter the TAC-KBP Entity Linking competition. Because most of us --and in particular, the developers among us –were undergraduates with little experience in Natural Language Processing, and because we knew we had only two months to pull together a system, we decided that we would try to use existing resources as much as possible. Our aim was to get an existing entity-linking system and modify it in order to improve output results. We were especially interested in generalizing the entitylinking system so that it would be useful for more than just Wikipedia’s domain. SAIC’s customers will often be interested in people who keep a low profile and who would be unlikely to have an entry in Wikipedia. 1.2 Harnessing Existing Resources We were fortunate that the researchers who had developed CUNY-BLENDER, the CUNY’s entry to multiple TAC-KBP tracks (entity linking and slot filling) in 2010 [Chen et al., 2010] had made CUNY-BLENDER’s codebase available to anyone who wanted to use it. We decided to build our system on top of the CUNY-BLENDER pipeline. Originally, we had envisioned that DOMINO would be a superset of CUNY-BLENDER. We had hoped to quickly get CUNY-BLENDER running, establish a baseline, and then spend most of our time experimenting with new features which would enhance Domino’s performance. In fact, we needed to make significant adjustments and modifications to CUNY-BLENDER. As a result, while Domino is based on CUNY-BLENDER, it is neither a subset nor a superset of it. 1.3 Overview of CUNY-BLENDER The architecture for the CUNY-BLENDER pipeline is shown below. Figure 1: CUNY-BLENDER’S architecture The general procedure for entity linking is as follows:
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 | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,010 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,055 | 0,048 |
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 ».